{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7765UFHoyGx6"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "KVtTDrUNyL7x"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "r0_fqL3ayLHX"
      },
      "source": [
        "# Gradient Boosted Trees: Model understanding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PS6_yKSoyLAl"
      },
      "source": [
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/alpha/tutorials/estimators/boosted_trees_model_understanding\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r2/tutorials/estimators/boosted_trees_model_understanding.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/docs/tree/master/site/en/r2/tutorials/estimators/boosted_trees_model_understanding.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dW3r7qVxzqN5"
      },
      "source": [
        "\n",
        "For an end-to-end walkthrough of training a Gradient Boosting model check out the [boosted trees tutorial](./boosted_trees). In this tutorial you will:\n",
        "\n",
        "* Learn how to interpret a Boosted Tree model both *locally* and *globally*\n",
        "* Gain intution for how a Boosted Trees model fits a dataset\n",
        "\n",
        "## How to interpret Boosted Trees models both locally and globally\n",
        "\n",
        "Local interpretability refers to an understanding of a model’s predictions at the individual example level, while global interpretability refers to an understanding of the model as a whole. Such techniques can help machine learning (ML) practitioners detect bias and bugs during the model development stage.\n",
        "\n",
        "For local interpretability, you will learn how to create and visualize per-instance contributions. To distinguish this from feature importances, we refer to these values as directional feature contributions (DFCs).\n",
        "\n",
        "For global interpretability you will retrieve and visualize gain-based feature importances, [permutation feature importances](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) and also show aggregated DFCs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eylrTPAN3rJV"
      },
      "source": [
        "## Load the titanic dataset\n",
        "You will be using the titanic dataset, where the (rather morbid) goal is to predict passenger survival, given characteristics such as gender, age, class, etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "KuhAiPfZ3rJW"
      },
      "outputs": [],
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Load dataset.\n",
        "dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\n",
        "dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')\n",
        "y_train = dftrain.pop('survived')\n",
        "y_eval = dfeval.pop('survived')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "sp1ShjJJeyH3"
      },
      "outputs": [],
      "source": [
        "!pip install tensorflow==2.0.0-alpha0\n",
        "\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(123)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3ioodHdVJVdA"
      },
      "source": [
        "For a description of the features, please review the prior tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "krkRHuMp3rJn"
      },
      "source": [
        "## Create feature columns, input_fn, and the train the estimator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JiJ6K3hr1lXW"
      },
      "source": [
        "### Preprocess the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "udMytRJC05oW"
      },
      "source": [
        "Create the feature columns, using the original numeric columns as is and one-hot-encoding categorical variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "upaNWxcF3rJn"
      },
      "outputs": [],
      "source": [
        "fc = tf.feature_column\n",
        "CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',\n",
        "                       'embark_town', 'alone']\n",
        "NUMERIC_COLUMNS = ['age', 'fare']\n",
        "\n",
        "def one_hot_cat_column(feature_name, vocab):\n",
        "  return fc.indicator_column(\n",
        "      fc.categorical_column_with_vocabulary_list(feature_name,\n",
        "                                                 vocab))\n",
        "feature_columns = []\n",
        "for feature_name in CATEGORICAL_COLUMNS:\n",
        "  # Need to one-hot encode categorical features.\n",
        "  vocabulary = dftrain[feature_name].unique()\n",
        "  feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\n",
        "\n",
        "for feature_name in NUMERIC_COLUMNS:\n",
        "  feature_columns.append(fc.numeric_column(feature_name,\n",
        "                                           dtype=tf.float32))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9rTefnXe1n0v"
      },
      "source": [
        "### Build the input pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-UOlROp33rJo"
      },
      "source": [
        "Create the input functions using the `from_tensor_slices` method in the [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) API to read in data directly from Pandas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "9dquwCQB3rJp"
      },
      "outputs": [],
      "source": [
        "# Use entire batch since this is such a small dataset.\n",
        "NUM_EXAMPLES = len(y_train)\n",
        "\n",
        "def make_input_fn(X, y, n_epochs=None, shuffle=True):\n",
        "  def input_fn():\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((X.to_dict(orient='list'), y))\n",
        "    if shuffle:\n",
        "      dataset = dataset.shuffle(NUM_EXAMPLES)\n",
        "    # For training, cycle thru dataset as many times as need (n_epochs=None).\n",
        "    dataset = (dataset\n",
        "      .repeat(n_epochs)\n",
        "      .batch(NUM_EXAMPLES))\n",
        "    return dataset\n",
        "  return input_fn\n",
        "\n",
        "# Training and evaluation input functions.\n",
        "train_input_fn = make_input_fn(dftrain, y_train)\n",
        "eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HttfNNlN3rJr"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 377
        },
        "colab_type": "code",
        "id": "tgEzMtlw3rJu",
        "outputId": "809ea8f0-2178-4890-904a-3477930e1105"
      },
      "outputs": [
        {
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            "text/plain": [
              "                               0\n",
              "accuracy                0.806818\n",
              "accuracy_baseline       0.625000\n",
              "auc                     0.866606\n",
              "auc_precision_recall    0.849128\n",
              "average_loss            0.421549\n",
              "global_step           100.000000\n",
              "label/mean              0.375000\n",
              "loss                    0.421549\n",
              "precision               0.755319\n",
              "prediction/mean         0.384944\n",
              "recall                  0.717172"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "params = {\n",
        "  'n_trees': 50,\n",
        "  'max_depth': 3,\n",
        "  'n_batches_per_layer': 1,\n",
        "  # You must enable center_bias = True to get DFCs. This will force the model to\n",
        "  # make an initial prediction before using any features (e.g. use the mean of\n",
        "  # the training labels for regression or log odds for classification when\n",
        "  # using cross entropy loss).\n",
        "  'center_bias': True\n",
        "}\n",
        "\n",
        "est = tf.estimator.BoostedTreesClassifier(feature_columns, **params)\n",
        "# Train model.\n",
        "est.train(train_input_fn, max_steps=100)\n",
        "\n",
        "# Evaluation.\n",
        "results = est.evaluate(eval_input_fn)\n",
        "clear_output()\n",
        "pd.Series(results).to_frame()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TSZYqNcRuczV"
      },
      "source": [
        "## Model interpretation and plotting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "BjcfLiI3uczW"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns_colors = sns.color_palette('colorblind')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ywTtbBvBuczY"
      },
      "source": [
        "## Local interpretability\n",
        "Next you will output the directional feature contributions (DFCs) to explain individual predictions using the approach outlined in [Palczewska et al](https://arxiv.org/pdf/1312.1121.pdf) and by Saabas in [Interpreting Random Forests](http://blog.datadive.net/interpreting-random-forests/) (this method is also available in scikit-learn for Random Forests in the [`treeinterpreter`](https://github.com/andosa/treeinterpreter) package). The DFCs are generated with:\n",
        "\n",
        "`pred_dicts = list(est.experimental_predict_with_explanations(pred_input_fn))`\n",
        "\n",
        "(Note: The method is named experimental as we may modify the API before dropping the experimental prefix.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "TIL93B4sDRqE"
      },
      "outputs": [],
      "source": [
        "pred_dicts = list(est.experimental_predict_with_explanations(eval_input_fn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
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        "id": "tDPoRx_ZaY1E",
        "outputId": "a20f119f-7a50-4392-dedd-b4a713b6fe8b"
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      "outputs": [
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              "      \u003ctd\u003e0.135820\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eembark_town\u003c/th\u003e\n",
              "      \u003ctd\u003e264.0\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.006554\u003c/td\u003e\n",
              "      \u003ctd\u003e0.025388\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.050465\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.013431\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.012491\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.002658\u003c/td\u003e\n",
              "      \u003ctd\u003e0.062315\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003efare\u003c/th\u003e\n",
              "      \u003ctd\u003e264.0\u003c/td\u003e\n",
              "      \u003ctd\u003e0.022725\u003c/td\u003e\n",
              "      \u003ctd\u003e0.083313\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.238293\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.023252\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.005715\u003c/td\u003e\n",
              "      \u003ctd\u003e0.054752\u003c/td\u003e\n",
              "      \u003ctd\u003e0.221681\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003en_siblings_spouses\u003c/th\u003e\n",
              "      \u003ctd\u003e264.0\u003c/td\u003e\n",
              "      \u003ctd\u003e0.002379\u003c/td\u003e\n",
              "      \u003ctd\u003e0.018807\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.120684\u003c/td\u003e\n",
              "      \u003ctd\u003e0.002746\u003c/td\u003e\n",
              "      \u003ctd\u003e0.003192\u003c/td\u003e\n",
              "      \u003ctd\u003e0.005547\u003c/td\u003e\n",
              "      \u003ctd\u003e0.066210\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eparch\u003c/th\u003e\n",
              "      \u003ctd\u003e264.0\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000140\u003c/td\u003e\n",
              "      \u003ctd\u003e0.003230\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.052090\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000235\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000285\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000497\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000574\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003ealone\u003c/th\u003e\n",
              "      \u003ctd\u003e264.0\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
            ],
            "text/plain": [
              "                    count      mean       std       min       25%       50%  \\\n",
              "age                 264.0 -0.027537  0.080741 -0.139372 -0.076765 -0.052056\n",
              "sex                 264.0  0.006734  0.106714 -0.119415 -0.072686 -0.071646\n",
              "class               264.0  0.016440  0.091105 -0.054638 -0.045972 -0.045116\n",
              "deck                264.0 -0.016943  0.029100 -0.060259 -0.041967 -0.029271\n",
              "embark_town         264.0 -0.006554  0.025388 -0.050465 -0.013431 -0.012491\n",
              "fare                264.0  0.022725  0.083313 -0.238293 -0.023252 -0.005715\n",
              "n_siblings_spouses  264.0  0.002379  0.018807 -0.120684  0.002746  0.003192\n",
              "parch               264.0  0.000140  0.003230 -0.052090  0.000235  0.000285\n",
              "alone               264.0  0.000000  0.000000  0.000000  0.000000  0.000000\n",
              "\n",
              "                         75%       max\n",
              "age                 0.002137  0.372113\n",
              "sex                 0.136922  0.179607\n",
              "class               0.034190  0.227794\n",
              "deck                0.003616  0.135820\n",
              "embark_town        -0.002658  0.062315\n",
              "fare                0.054752  0.221681\n",
              "n_siblings_spouses  0.005547  0.066210\n",
              "parch               0.000497  0.000574\n",
              "alone               0.000000  0.000000"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create DFC Pandas dataframe.\n",
        "labels = y_eval.values\n",
        "probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])\n",
        "df_dfc = pd.DataFrame([pred['dfc'] for pred in pred_dicts])\n",
        "df_dfc.describe().T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EUKSaVoraY1C"
      },
      "source": [
        "A nice property of DFCs is that the sum of the contributions + the bias is equal to the prediction for a given example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Hd9VuizRaY1H"
      },
      "outputs": [],
      "source": [
        "# Sum of DFCs + bias == probabality.\n",
        "bias = pred_dicts[0]['bias']\n",
        "dfc_prob = df_dfc.sum(axis=1) + bias\n",
        "np.testing.assert_almost_equal(dfc_prob.values,\n",
        "                               probs.values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tx5p4vEhuczg"
      },
      "source": [
        "Plot DFCs for an individual passenger. Let's make the plot nice by color coding based on the contributions' directionality and add the feature values on figure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "6z_Tq1Pquczj"
      },
      "outputs": [],
      "source": [
        "# Boilerplate code for plotting :)\n",
        "def _get_color(value):\n",
        "    \"\"\"To make positive DFCs plot green, negative DFCs plot red.\"\"\"\n",
        "    green, red = sns.color_palette()[2:4]\n",
        "    if value \u003e= 0: return green\n",
        "    return red\n",
        "\n",
        "def _add_feature_values(feature_values, ax):\n",
        "    \"\"\"Display feature's values on left of plot.\"\"\"\n",
        "    x_coord = ax.get_xlim()[0]\n",
        "    OFFSET = 0.15\n",
        "    for y_coord, (feat_name, feat_val) in enumerate(feature_values.items()):\n",
        "        t = plt.text(x_coord, y_coord - OFFSET, '{}'.format(feat_val), size=12)\n",
        "        t.set_bbox(dict(facecolor='white', alpha=0.5))\n",
        "    from matplotlib.font_manager import FontProperties\n",
        "    font = FontProperties()\n",
        "    font.set_weight('bold')\n",
        "    t = plt.text(x_coord, y_coord + 1 - OFFSET, 'feature\\nvalue',\n",
        "    fontproperties=font, size=12)\n",
        "\n",
        "def plot_example(example):\n",
        "  TOP_N = 8 # View top 8 features.\n",
        "  sorted_ix = example.abs().sort_values()[-TOP_N:].index  # Sort by magnitude.\n",
        "  example = example[sorted_ix]\n",
        "  colors = example.map(_get_color).tolist()\n",
        "  ax = example.to_frame().plot(kind='barh',\n",
        "                          color=[colors],\n",
        "                          legend=None,\n",
        "                          alpha=0.75,\n",
        "                          figsize=(10,6))\n",
        "  ax.grid(False, axis='y')\n",
        "  ax.set_yticklabels(ax.get_yticklabels(), size=14)\n",
        "\n",
        "  # Add feature values.\n",
        "  _add_feature_values(dfeval.iloc[ID][sorted_ix], ax)\n",
        "  return ax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 443
        },
        "colab_type": "code",
        "id": "Ht1P2-1euczk",
        "outputId": "61f41998-2815-44dc-cdc3-bae2f04e4525"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Pt9WT04tw11cKve5WCay3tzcVKlRgy5Ytt4zf09MTFxcXTp06lS3BL1OmTLbENjY29pYJ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\nEsBQMAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe2c8f61450\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot results.\n",
        "ID = 182\n",
        "example = df_dfc.iloc[ID]  # Choose ith example from evaluation set.\n",
        "TOP_N = 8  # View top 8 features.\n",
        "sorted_ix = example.abs().sort_values()[-TOP_N:].index\n",
        "ax = plot_example(example)\n",
        "ax.set_title('Feature contributions for example {}\\n pred: {:1.2f}; label: {}'.format(ID, probs[ID], labels[ID]))\n",
        "ax.set_xlabel('Contribution to predicted probability', size=14)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aPXgWyFcfzAc"
      },
      "source": [
        "The larger magnitude contributions have a larger impact on the model's prediction. Negative contributions indicate the feature value for this given example reduced the model's prediction, while positive values contribute an increase in the prediction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0swvlkZFaY1Z"
      },
      "source": [
        "You can also plot the example's DFCs compare with the entire distribution using a voilin plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "zo7rNd1v_5e2"
      },
      "outputs": [],
      "source": [
        "# Boilerplate plotting code.\n",
        "def dist_violin_plot(df_dfc, ID):\n",
        "  # Initialize plot.\n",
        "  fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
        "\n",
        "  # Create example dataframe.\n",
        "  TOP_N = 8  # View top 8 features.\n",
        "  example = df_dfc.iloc[ID]\n",
        "  ix = example.abs().sort_values()[-TOP_N:].index\n",
        "  example = example[ix]\n",
        "  example_df = example.to_frame(name='dfc')\n",
        "\n",
        "  # Add contributions of entire distribution.\n",
        "  parts=ax.violinplot([df_dfc[w] for w in ix],\n",
        "                 vert=False,\n",
        "                 showextrema=False,\n",
        "                 widths=0.7,\n",
        "                 positions=np.arange(len(ix)))\n",
        "  face_color = sns_colors[0]\n",
        "  alpha = 0.15\n",
        "  for pc in parts['bodies']:\n",
        "      pc.set_facecolor(face_color)\n",
        "      pc.set_alpha(alpha)\n",
        "\n",
        "  # Add feature values.\n",
        "  _add_feature_values(dfeval.iloc[ID][sorted_ix], ax)\n",
        "\n",
        "  # Add local contributions.\n",
        "  ax.scatter(example,\n",
        "              np.arange(example.shape[0]),\n",
        "              color=sns.color_palette()[2],\n",
        "              s=100,\n",
        "              marker=\"s\",\n",
        "              label='contributions for example')\n",
        "\n",
        "  # Legend\n",
        "  # Proxy plot, to show violinplot dist on legend.\n",
        "  ax.plot([0,0], [1,1], label='eval set contributions\\ndistributions',\n",
        "          color=face_color, alpha=alpha, linewidth=10)\n",
        "  legend = ax.legend(loc='lower right', shadow=True, fontsize='x-large',\n",
        "                     frameon=True)\n",
        "  legend.get_frame().set_facecolor('white')\n",
        "\n",
        "  # Format plot.\n",
        "  ax.set_yticks(np.arange(example.shape[0]))\n",
        "  ax.set_yticklabels(example.index)\n",
        "  ax.grid(False, axis='y')\n",
        "  ax.set_xlabel('Contribution to predicted probability', size=14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PiLw2tlm_9aK"
      },
      "source": [
        "Plot this example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 434
        },
        "colab_type": "code",
        "id": "VkCqraA2uczm",
        "outputId": "bc6cc132-a525-4158-f870-ae341e195b19"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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ONmzY4ny9+upbpSrYCiGEEKJskZbbcqRu3XrExl7C7t3fAtC3b388Hg/R0dUA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\nrkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe2c9220ed0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dist_violin_plot(df_dfc, ID)\n",
        "plt.title('Feature contributions for example {}\\n pred: {:1.2f}; label: {}'.format(ID, probs[ID], labels[ID]))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TVJFM85SAWVq"
      },
      "source": [
        "Finally, third-party tools, such as [LIME](https://github.com/marcotcr/lime) and [shap](https://github.com/slundberg/shap), can also help understand individual predictions for a model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PnNXH6mZuczr"
      },
      "source": [
        "## Global feature importances\n",
        "\n",
        "Additionally, you might want to understand the model as a whole, rather than studying individual predictions. Below, you will compute and use:\n",
        "\n",
        "1. Gain-based feature importances using `est.experimental_feature_importances`\n",
        "2. Permutation importances\n",
        "3. Aggregate DFCs using `est.experimental_predict_with_explanations`\n",
        "\n",
        "Gain-based feature importances measure the loss change when splitting on a particular feature, while permutation feature importances are computed by evaluating model performance on the evaluation set by shuffling each feature one-by-one and attributing the change in model performance to the shuffled feature.\n",
        "\n",
        "In general, permutation feature importance are preferred to gain-based feature importance, though both methods can be unreliable in situations where potential predictor variables vary in their scale of measurement or their number of categories and when features are correlated ([source](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-307)). Check out [this article](http://explained.ai/rf-importance/index.html) for an in-depth overview and great discussion on different feature importance types."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3ocBcMatuczs"
      },
      "source": [
        "### 1. Gain-based feature importances"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gMaxCgPbBJ-j"
      },
      "source": [
        "Gain-based feature importances are built into the TensorFlow Boosted Trees estimators using `est.experimental_feature_importances`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 401
        },
        "colab_type": "code",
        "id": "pPTxbAaeuczt",
        "outputId": "e5a33249-938e-4708-8e79-8db4efc41abc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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+fn5atmxZjsccOXIk1zEDAwP17bffasCAASpXrpzDYwYPHqyQkJBs26xWa35K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\nYFqEXQAAAJgWYRcAAACmRdgFAACAaf0/WW3QRBr7SvQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe2c8c14250\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "importances = est.experimental_feature_importances(normalize=True)\n",
        "df_imp = pd.Series(importances)\n",
        "\n",
        "# Visualize importances.\n",
        "N = 8\n",
        "ax = (df_imp.iloc[0:N][::-1]\n",
        "    .plot(kind='barh',\n",
        "          color=sns_colors[0],\n",
        "          title='Gain feature importances',\n",
        "          figsize=(10, 6)))\n",
        "ax.grid(False, axis='y')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GvfAcBeGuczw"
      },
      "source": [
        "### 2. Average absolute DFCs\n",
        "You can also average the absolute values of DFCs to understand impact at a global level."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 401
        },
        "colab_type": "code",
        "id": "JkvAWLWLuczx",
        "outputId": "2a65a002-ee33-4f19-ded8-abe8500a167e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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21aJFi/Tee+85x87r1/fXX3/V8OHD1bhxY3Xp0kXh4eHq1q3bLY8v6fjz3P0Ku+cWg5v4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\ntP4frJ1/n33YZQsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe2c89ba650\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot.\n",
        "dfc_mean = df_dfc.abs().mean()\n",
        "N = 8\n",
        "sorted_ix = dfc_mean.abs().sort_values()[-N:].index  # Average and sort by absolute.\n",
        "ax = dfc_mean[sorted_ix].plot(kind='barh',\n",
        "                       color=sns_colors[1],\n",
        "                       title='Mean |directional feature contributions|',\n",
        "                       figsize=(10, 6))\n",
        "ax.grid(False, axis='y')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Z0k_DvPLaY1o"
      },
      "source": [
        "You can also see how DFCs vary as a feature value varies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 296
        },
        "colab_type": "code",
        "id": "ZcIfN1IpaY1o",
        "outputId": "84f8e137-d188-4b24-9fab-afa7e253b11d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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AZu+71iGViJCmTEByohTyBAniJCKIRQzEYhEkIgYcnPNSOsw26Nut0BktroJa\n39YJfVsnzlTpPK4pYgAwQJyYweTR2aiuN0KTnoj4Pvo/BtJm3l+zh76tE9VaI6rqnYGmur4N7Wab\nz+skJ0oxZlgmhmQpMCIvlebYEEGIxv4kvz9Bbm6ux+OmpiZ88sknHouGEqdKbatrjbZuHebgT0Br\n1Jtw6nILTlW24EKNod/gAgDSOBEGZyqQq05CnjoJOapEZCTLcFNeGpqb2/t9bW8dFhu0zSbUNrej\ntqkDtc0dqGvucNWiWA4Ax6GT5bD3i2rs/aIaDICMlATkZCicNaSMRGSly6FOlQ+ozby72aPNZEV1\nfRu+OKV11mbqjWht953v8XFi5GUlIT8rCUM0SuRrkpCZIkNmpjIihhBHY7s/8U0Ic/eCze95PL6c\nPn0amzZtitiFQkM1j+f5TV9A3+Zd4L29auqAr330XD227zuPThvrc+RcYoIEGckyZCQnID05ARnJ\nCchIkUGdKoM6Ve6zXyKY8zXazTbUNXegtqndFYxqmzvQZvJdy+imTJQiK02OrDQZ1GlyZKXJkSSX\nQixiIGIY539Fzv8yIudM8rqu69c1d6CupQM6Y6fPa0vEIuSqFe4gk5UETXpiyPMilPiYRxIpecEH\nygsn3ufx+DJixAiaw+NDsJf2bzfb8H+ntfi/M/Wo6TWUl2GASSOzcHNBOobnpoS9H6LRYMLWvWe8\nhlIbO6w4X6PHR19egdFkhd3BgeU4mLo68Y0dVhg7rLh41TCg9xcxDAapEpGvSUK+RokhWUrkqBKj\nbtRZNLb7k9jhd+D58ssvPR5bLBb885//RGFhYdATFenipSKYfN98+43jOFyuM+KzE7U4dq4Rdodn\nMBMxzruPOLEIi2cW93EV/vU1lFqZKMU35xsBBkhWxLvu0udPL0K9zoQGnQn1Pf416Mxen7m31KR4\nZKfLoeka3DBIpUBupiIm5tFEY7v/QNBOtpHF72/riy++6PFYLpejqKgIv/3tb4OeqEi3dM7N+M32\nb9FznNT9k/zbMM/cacdXZxvw2Ylaj4mKCVIxJo7MwrcXGmC22CESicBxHBLihVXI9jeU2tddukIW\nh8KcZBTmeBYSLMfB0TWkm2U5sBxcfzscLOQJcT6HcseKaGz3HwjayTay+P3LPXToUCjT4VJdXY1V\nq1bBYDAgJSUF69ev9xrYwLIs1q1bhy+++AIikQiLFy8W1CKmBZpkjB2u8miDr9db+n1Nc6sZnxy7\nii9Oa9EP/XLVAAAgAElEQVTZY2hvrlqBKWNzMKFYjQSpBHeMyhL0Ss/9bYEQyF26iGEgkogR1+u4\nrs2C9w9fivlOdSGsOi4ktJNtZAnoltFoNLo2gsvMzMSdd96J5OTgVmfXrFmDxx9/HDNnzsTevXux\nevVqbN++3eOcvXv34urVqzhw4AB0Oh3Kyspw++23Izs7O6hpGQh/2+CvNrZj39ErOHa20bXAZJxE\nhNtGZGLK2EEYokny2I5C6Cs997cFQjDu0gOdTEejv2JDrO5kG6n8HtX25ZdfYvny5RgyZAiys7Oh\n1WpRWVmJDRs2YNKkSUFJjE6nw4wZM3D06FEwDAOWZTFhwgTs378fqamprvOefvppPPDAA7jnnnsA\nAOvWrUNOTg6eeuqpgN4vlKtT9xx1ZLPZYbY6oE5NdBV+TXozPvqqBqcrW1yvkYgZZKXJsWTWSAxS\n9T9ypHeB+oObs/DOJxe82rivt8vin/edR0WtAZZOFgwDJMmlWP6gcNvH1//1BMxWdxCXSSVY+Zjv\n3SErta14bcdx2O0cRCIGqpQEDM5U9BuoaPSSWyTlRaj7eCIpL0KJ91Ft69atw9q1a3Hfffe5ju3b\ntw+/+tWv8PHHHw84IQCg1WqhVqtdd/gikQiZmZmor6/3CDx1dXUetRuNRgOtVuvzmkajEUaj5wrC\nYrEYGo0mKGnuS8+7e2NHJ+TxEpitdujaLPjV//vaY3hxglQMeYIECpmzYan8SFWfhWN3ILlQY4Dd\nwSI9OQGmThu27DkDcPBq477eLosVta0wdzqbJTgOaO2wCrp9PJDmuk27TsNud95YsCyHJoMFKQqq\n7UQjobcERButVguHo9f6hUollEqlX6/3O/A0NjZi+vTpHsfuvvturF692t9LhMX27duxceNGj2M5\nOTk4dOhQUCJ3X1SqJMyVSPDqn46htd2K1g4r4sRij8Umh+el4oEpQ1H+78swd7rv4jvtHFSqJJ/X\nffuj89C3d8LWtWq4od2KrHQ5HA4OcRLnQAOGYWC1sVCpkmB1cB6jvHpe2+rg0Lu+xzBwvVaIlj48\nFtv2noGxwwqlXIpFs0uQ0ccKA1absxbHcs7PxbIcMlJk1/1svp5vNpix7R9nYDRd/31vFB/vESih\nfg/CgfLCbd68eaitrfU4tmzZMixfvtyv1/sdeEpLS7Fjxw4sWLDAdezdd9/12KV0oDQaDRoaGsBx\nnKuprbGxEVlZWR7nZWdno66uDiUlzsU1tVqta0253hYuXIiysjKPY91bOYSyqQ0Ann/jc/cDDuhk\nPe8QLlzR4833jsNsdcDBchAx7tWqj566hq9O1+HT4+6aXGqSFHYHh+REKURgYOtazNNqc0AsduZX\nd01AmhCHpqY2SMUMDDZ323dKotTVZCAVM+g9hZLjnCscNDW1Bdw/wld/ylP3FrnTa7P32QQijRPB\n0ukMOuAAiYRB6eT8fptMejap9Pw8DfoOyOMlkEjEMLRZsPlvJ4Leud+zeTZU7xEIal5yo7xw6m5q\n27Fjh88aj7/6DTyPPfaYq9mLZVm8++67+OMf/wi1Wo2Ghga0tLT43JX0RqWlpaGoqAjl5eWYNWsW\nysvLUVxc7NHMBgAzZszA+++/j7vvvht6vR4HDx70WDm7p0Cqf8F08Zp/EyHbLXZIuvpBWc45P4fj\nnM1EvVc/0LdZIYsXo8VoQVpSPHRtnZCIRchMkaF08hCvPh6g/w797sVMK6559vF0vzbQjvxtH57F\nhRr35+6w2PFfj/ruf/FloIGr9+vnTx/ulSeBXM/j81vs6LSxUKXIQjZhkyaFkkgx0K6KfgNP7yHK\nDz/88IDezB8vv/wyVq1ahc2bNyM5Odm1AOmSJUuwYsUKjBw5ErNnz8Z3332He+65BwzDYOnSpRg0\naFDI0+YPo8mK8iPVOPTtNb9fIxKJANbZz9I9w76v4aAcy0EiFiFJHg9NeqJH4exr++zr7bL404fG\n9Hk3F2hBWHG11WMZn4oAVyEY6PLvvV//5Zn6AbX79/z8YpEIDkd3X1hoJmzSpFASK/r9ZvduouJD\nQUEB3n//fa/jv//9711/i0QivPzyyzym6vo6rQ7s/7oG+47W+FxiPxDdw0F9rX7gYDnIEkR9juQK\npkALQg6eG9wF2og50Dv+YNcYen7+FEUczFYHZFJJyCZs0qRQEiv6LUn27Nnj6sP5+9//3ud5Dz74\nYHBTFUEcLIsvTmmx54uqPldC7o9CJgHLAnESQCQSw+HgXM1Cr2z/1uv8OImYt3koPQtCqYSB1c5i\n/V9PQCphgK4BDD2bxBITxK7CngGQmBDYqgoDveMPdo2h5+dXJSeEfA4QTQolsaLfX+Y///lPV+D5\nxz/+4fMchmFiMvBwHIeTl5rx988uQ9tiAuBsJrve+mLJif7Pk0mQimDp0eTGMM5tBVKT+FkItGdB\n2LPj+0qD8/Nmpso9msSeuG8Etuw5A4eDg1jM4In7RgT0fgO94w92jUGIgYDWJCPRoN/A84c//AGA\ns5B99dVXodFoIJFQu/Pl2la8f/gSKq61AnDe3f+gJAulkwvwX1v+r8/XMQhsnozd4dlYxXFAZoos\nLE0wPZuxWJbz2aT12fFaSMQiiBjnhM3PTtT67Hfqy0ALeiEGimCjNclINPArijAMg/vvvx/Hjx8P\ndXoErV5nwgf/uoxve2zyVlKQhofuKsTgTP/mBDGM/9smSEQMHCzn6jiJl4hwU3YSXtjkDm6PTCnA\nrcVZIR/G3LMZS9Q97hueHe1V2jbY7c4h3XY7i6o638NPhbaMTc/0ZKTIUDo5X7DL6tCaZCQa+L2g\n0YgRI1BVVRXKtAhWu9mGHQcuYvUfj7qCTp46CS/MHYPnHh7jEXRuyup7wl/XppyI83NvGFmCBBIR\ngziJcwtqaZwIOw9Xepyz83ClazSX2WpHo8GMnQcrAv+Q1zH3h0OhTpVBJpVgaE4yhg1OgUwq8ayB\nMfAoFL0mCXXhI72B6JkebUt72NPTn3ipc1VyALQmGYlYfreb3XbbbfjP//xPlJWVISsry2Phymjt\n42FZDv86WYvd/65Cu9m5xE1GcgLm3FmA20aoIeqRB913zZfrzf1eU8QA2Rlyv96/94KbWWlyGK94\nD1HmY/6HP81YQzRKVFwzgOOcMWeIxvf8KaHNVxFaenrrWSNTp8jAsoDNLszVyQnxh9+B5/jx48jJ\nyfHacTRaBxdcqNHjr59WuPbESZCKMev2IZh2yyDESbzvMrvvmvuTl+VccoPrqgpcr8mp9/pT6/96\nwud1hTL/Y8GM4X517gslvUJNT2895ydxHIdhg5Ojvi+LRDe/f2HvvPNOKNMhGDqjc7+XY+caXcfu\nuFmDB+4sQHIf20rr2iy4cNUAm/367e09C7ZAJ0wqZBIoEhi0W9yDDrr7eIQw/8Pfzn2hzVfpmZ7u\nPh4hEXqNjJBABbRW2549e7yOz5kzB7t27QpqosLBanPg46M1+OirK7B2BZCCbCUe++EwFGT3v+TO\ne59WwO5gXTPb+9J78mGgBYqzgIRXgR2Mjno+O/yFNvqsZ3qEuCaX0GtkhATK72/wlStXvI5xHIdr\n1/xfGkZo3vnkAmZMGIyqujbsPHQJLUbnLqHJiVI8eNdNmFSS5dGP05d2sx1pSfFoabXA5uh7vn7v\n1QYCLVB8Fdg959fcyDIz3Qa6XA0JHaHVEAkZqOsGnpUrVwIAbDab6+9utbW1KCwsDE3KeFCv78Da\nP33j2htHLGJwz62DMfMH+ZDF+39X2R1ARCIRGLA+l4rRpHkPKAhGgRKsZhhqzhEuodUQCRmo65au\nubm5Pv8GgHHjxmHGjBnBTxVPmvQWV9AZfVM65k4bCrWPAHE93QGk2WCBLF6CNGU8mlst6LQ6EC8V\ngwGgSvUeZh2MAiVYzTDUnEMI4ct1S5dly5YBAEaPHo3JkyeHPEF8i48T45nSEoy6Kf2Gr9EdQHo2\ne3Gcc5sBjuPAiLqXzQy+YDXDUHMOIYQvft/WTp48GZWVlTh//jxMJpPHc5E6nDorXY7nHhmNzJTA\nazm+eCyqGSeBKkUKcddkUastNIEnWM0w1JxDCOGL34HnrbfewqZNm1BUVISEBPdop0iex/Pcw2OC\nugOpr0U1gdDt30IIIZHI79Jw+/bt+Nvf/oaioqLrn0wwY2Kux6oDC2YMD3eSCCFEEPxe6CkhIQEF\nBQWhTEtU+firGiTJpVClypAkl+KTozXhThIhhAiC34FnxYoVeOWVV9DY2AiWZT3+EW80PJkQQnzz\nu6lt1apVAIC//e1vrmMc59yX5dy5c8FPWQTquUmXzeFAujIB8VJJzPTxCG27g/5EUloJiTZ+l4YH\nDx4MZTqiQs9NulgHhxajBTdlp8TM8ORIWv0gktJKSLTxO/Dk5OQAAFiWRXNzMzIyMiASxcZeIP7e\nHffcpEssFkEiFnktkxPNIql5MZLSSki08TtytLe3Y+XKlRg1ahT+4z/+A6NGjcLPf/5ztLUJa0HF\nUPB347JY36RLIZN4fP7u5kVdmwWbd5/G+r+ewObdp6Frs4QzmQD6TishJPT8LhlfeeUVmM1mlJeX\n49SpUygvL4fZbMYrr7wSyvSF1DufXPCrEPT37njpnJuhkMdBIhZBIY+LuU26eu5S2nNnUqHtOAr0\nnVZCSOj5fZv373//G59++ilkMueaY0OGDMFvfvMb3H333SFLXKhVXDWg2WDGiodG93uev+uY9d64\nLdb0tfqBEJu1aKUGQsLH7xpPfHw8dDqdxzG9Xg+pVBr0RPHFwXGo1Bqvex7dHQ8MNWsRQnryuwR4\n8MEH8dRTT+GJJ55AdnY26urq8Kc//QkPPfRQKNMXUhzH+bV2J90dDwwtQEoI6cnvwPPMM89ArVaj\nvLwcjY2NyMzMxOLFiyM68EjEIgzRJIU7GVGPAjchpCe/A8+vf/1r3HffffjTn/7kOnb8+HH8+te/\nxosvvhiKtIXc0EEpuHdi7vVPJIQQEjR+9/F8+OGHKCkp8ThWUlKCDz/8MCgJsVgs+NnPfoZ77rkH\n9913Hz777DOf5zU0NGDBggUYP378gFfFnj99OM1WJ4QQnvld42EYxmtdNofDEbS12rZt2waFQoH9\n+/fjypUrmDdvHg4cOOAaRdctMTERzz77LDo6OrBhw4agvDchhBD++F3jGT9+PN544w1XoGFZFhs2\nbMD48eODkpB9+/Zh7ty5AIC8vDyUlJTg888/9zpPoVBg/PjxXgGJEEJIZPC7xvPiiy/i6aefxh13\n3IHs7GxotVqoVCq89dZbQUlIXV0dsrOzXY81Gg20Wu2Ar2s0GmE0eg6ZFovF0Gg0A762P2gxSkJI\ntNFqtXA4HB7HlEollEqlX6/3O/BkZWVh9+7dOHXqFLRaLTQaDUaNGuX3em1z5szxCiTdq1sfOXLE\n32QEbPv27di4caPHsZycHBw6dAjp6YqQvW+3tz86D317JxiGgb69E3u+qMbPF9wa8vcNlEpFo/u6\nUV64UV64UV64zZs3D7W1tR7Hli1bhuXLl/v1+oBm8olEIowZMwZjxowJ5GUAgF27dvX7fE5ODurq\n6pCamgrAGVEnTpwY8Pv0tnDhQpSVlXkcE4vFAICWlvagbn3tS7PBDLuDQ/eEoWa9GU1NwlrfTqVK\nElyawoXywo3ywo3ywkkkYpCersCOHTt81nj8JZgp5NOnT8fOnTuxdu1aVFdX48yZM/jd737X5/kc\nx7lmw/cnkOpfKPi73A4hhESKgXZVCGb55EWLFqG1tRX33HMPnnnmGaxbtw5yuRwA8Oabb2Lnzp0A\nnIMa7rzzTvzsZz/DxYsXcdddd3k1pQkJLbdDCCGeGM6fakOU4qOpLRJQM4Ib5YUb5YUb5YVTd1Pb\ngK8ThLQQQgghfqPAQwghhFcUeAghhPCKAg8hhBBeUeAhhBDCK5pUQiIWLUdESGSiGg+JWO99WoFG\ngxlmqx2NBjN2HqwId5IIIX6gwEMiVrvZDoZhADi37Wgz2cOcIkKIPyjwkIilkElcyybRckSERA4K\nPCRizZiYizaTFU16M9pMVsygbcwJiQgUeEjE+virGiTJpVClypAkl+KTozXhThIhxA8UeEjEoj4e\nQiITBR4SsaiPh5DIRL9UIjgnLzVhy54zcDg4iMUMniktwZhCldd5c384FDsPVqDN5J7HQwgRPgo8\nRHC27DkDm50DA8Bm57BlzxlsfWGK13lpSQl4pvRm/hNICBkQamojguNwOIMOADBdjwkh0YMCDxEc\nsZhBd6jhuh4TQqIHBR4iOM+UliBOwoBhgDiJs4+HEBI9qI+HCM6YQpXPPh1CSHSgGg8hhBBeUeAh\nhBDCKwo8hBBCeEWBhxBCCK8o8BBCCOEVBR5CCCG8ouHUJOR0bRa892kF2s3uNdXSkhLCnSzexPrn\nJ6Q3qvGQkHvv0wo0GswwW+1oNJix82BFuJPEq1j//IT0RoGHhFys75sT65+fkN4E09RmsVjwi1/8\nAt9//z0kEglWrlyJu+66y+u8gwcPYtOmTbDZbACAOXPm4Mknn+Q5tSQQCpkEpk4bGIYBx3GQShhs\n3n06ZpqepHEiXKk3gYNz0dOhg1PCnSRCwkowgWfbtm1QKBTYv38/rly5gnnz5uHAgQOQyWQe56lU\nKmzduhUqlQrt7e2YM2cORo0ahVtuuSVMKSfX03vfHKudRaPBDIZhYOq0YefBiuje3oDjAAbgWA6M\niAFAq233h/rEop9gmtr27duHuXPnAgDy8vJQUlKCzz//3Ou8UaNGQaVybgqmUChQUFCAuro6XtNK\nAtO9b87Kx8biJ2U3w2pjY6rpyWrnkJkqR1Z6IjJT5bDaKPD0h/rEop9gAk9dXR2ys7NdjzUaDbRa\nbb+vuXz5Mk6dOoWJEyeGOnkkiGJty+pY+7wDRX1i0Y+3X8CcOXO8AgnHcWAYBkeOHAn4eo2NjVi6\ndCnWrFnjqgH5YjQaYTQaPY6JxWJoNJqA35MER6xtWR1rn3egevcJUqAWHq1WC4fD4XFMqVRCqVT6\n9XqG674VC7P7778f//3f/42RI0cCAH784x+jrKwM06dP9zq3paUFCxcuxJNPPokHHnig3+tu2LAB\nGzdu9DiWk5ODQ4cOBS/xhJCgaTaYsW3vGRg7rFDKpVg0uwQZKbLrv5DwZurUqaitrfU4tmzZMixf\nvtyv1wsm8GzcuBGNjY1Yu3Ytqqur8fjjj2P//v2Qy+Ue5+n1ejzxxBN49NFHXX1C/emvxtPS0g6W\nFcTHDyuVKglNTW3hToYgUF64UV64UV44iUQM0tMV0VPjMZvNWLVqFc6dOwexWIyVK1diyhTnZmBv\nvvkm1Go1HnnkEaxfvx5//etfMWTIEFdT3YIFC1BWVhbwe1LgcaIflRvlhRvlhRvlhVN34BkowQSe\ncKDA40Q/KjfKCzfKCzfKC6dgBR7BjGojhBASGyjwEEII4RUFHkIIIbyiwEMIIYRXFHgIIYTwigIP\nIYQQXlHgIYQQwisKPIQQQnhFgYcQQgivKPAQQgjhFQUeQgghvKLAQwghhFcUeAghhPCKAg8hhBBe\nUeAhhBDCKwo8hBBCeEWBhxBCCK8o8BBCCOEVBR5CCCG8osBDCCGEVxR4CCGE8IoCDyGEEF5R4CGE\nEMIrCjyEEEJ4RYGHEEIIryjwEEII4RUFHkIIIbyiwEMIIYRXknAnoJvFYsEvfvELfP/995BIJFi5\nciXuuusur/POnz+PX/7yl+A4Dna7HWPHjsXq1asRFxfHf6IJIYQETDCBZ9u2bVAoFNi/fz+uXLmC\nefPm4cCBA5DJZB7nFRQU4P3334dE4kz6s88+i507d+Lxxx8PR7IJIYQESDBNbfv27cPcuXMBAHl5\neSgpKcHnn3/udZ5UKnUFHavVCovFAoZheE0rIYSQGyeYwFNXV4fs7GzXY41GA61W6/PcxsZGlJaW\nYtKkSVAoFHjkkUf4SiYhhJAB4q2pbc6cOV6BhOM4MAyDI0eOBHStzMxM7NmzBxaLBf/1X/+F/fv3\n47777vN5rtFohNFo9DgmFouh0WggElFNqRvlhRvlhRvlhRvlhTsPtFotHA6Hx3NKpRJKpdKv6/AW\neHbt2tXv8zk5Oairq0NqaioA5webOHFiv69JSEjAvffei/Ly8j4Dz/bt27Fx40aPY+PGjcO7776L\n1NTEAD5BdEtPV4Q7CYJBeeFGeeFGeeH23HPP4fjx4x7Hli1bhuXLl/v1esE0tU2fPh07d+4EAFRX\nV+PMmTOYPHmy13lXr16FzWYD4OzjOXjwIIYNG9bndRcuXIiDBw96/Hv++efx6KOP9tmUF0u0Wi2m\nTp1KeQHKi54oL9woL9y0Wi0effRRPP/8817l6sKFC/2+jmBGtS1atAirVq3CPffcA7FYjHXr1kEu\nlwMA3nzzTajVajzyyCM4ceIE/vCHP0AsFsPhcOC2227D0qVL+7xuX9W/48ePe1UVY5HD4UBtbS3l\nBSgveqK8cKO8cHM4HDh+/DiysrIwaNCgG76OYAKPTCbDG2+84fO5Z5991vX3rFmzMGvWLL6SRQgh\nJMgE09RGCCEkNlDgIYQQwivxyy+//HK4ExEO8fHxmDBhAuLj48OdlLCjvHCjvHCjvHCjvHALRl4w\nHMdxQUwTIYQQ0i9qaiOEEMIrCjyEEEJ4FXOBp7q6GnPnzsWMGTMwd+5c1NTUhDtJvDEYDFiyZAnu\nvfdezJ49G88++yz0ej0A4OTJk5g9ezZmzJiBRYsWQafThTm1/Ni4cSOKiopw6dIlALGZD1arFS+/\n/DKmT5+OWbNm4aWXXgIQm7+Vw4cPo6ysDKWlpZg9ezYOHDgAIDby4rXXXsO0adM8fg9A/5/9hvOF\nizELFizgysvLOY7juH/84x/cggULwpwi/hgMBu7YsWOux6+99hr34osvchzHcXfffTd3/PhxjuM4\nbvPmzdwvfvGLsKSRT99//z23ePFibsqUKVxFRQXHcbGZD+vWreN+85vfuB63tLRwHBebv5Vbb72V\nu3TpEsdxHHf+/Hlu7NixHMfFRl58++23XH19PTd16lTX74Hj+v/sN5ovMRV4WlpauFtvvZVjWZbj\nOI5zOBzc+PHjOZ1OF+aUhccnn3zCPfnkk9ypU6e4mTNnuo7rdDpuzJgxYUxZ6HV2dnKPPPIId+3a\nNVfgicV86Ojo4MaPH8+ZTCaP47H6W5kwYYLrxuPYsWPc9OnTuZaWFm78+PExkxc9b8T6+x4M5Dsi\nmJUL+KDVaqFWq13794hEImRmZqK+vt61OGms4DgO7777LqZNmwatVoucnBzXc915YTQa/V5tNtK8\n+eabmD17tsfnjsV8qKmpQUpKCjZs2ICjR48iMTERK1asQEJCQkz+Vl5//XU888wzkMvl6OjowO9/\n/3totVpkZWXFXF4A/ZeZLMve8Hck5vp4iNPatWuRmJjY586tXBSPsj958iROnz6NRx991HWsr88b\nzfkAONfeunr1KkpKSvDBBx/ghRdewPLly2EymaL+s/fmcDjw+9//Hm+99RYOHTqELVu24Kc//SlM\nJlO4kxZ1YqrGo9Fo0NDQ4NoHiGVZNDY2IisrK9xJ49Vrr72GmpoabN26FYAzX2pra13P63Q6MAwT\ntXf5x44dQ1VVFaZNmwaO49DQ0IDFixdj/vz5MZUPAJCdnQ2JROLaVmTUqFFIS0tDfHw8GhsbY+q3\ncu7cOTQ1NWHMmDEAnNunyGQyxMfHx2y50V+Z2f3buZF8iakaT1paGoqKilBeXg4AKC8vR3FxcdRX\nl3t6/fXXcfbsWWzevNm1hXhJSQk6Oztd+2u89957uPfee8OZzJBasmQJPv/8cxw8eBCHDh2CWq3G\n22+/jUWLFsVUPgDO5sQJEya4NmOsqqpCS0sLCgoKYu63kpWVhfr6elRVVQEALl++jJaWFuTn58dc\nXnTXdvsrMwdSnsbcygWVlZVYtWoVjEYjkpOT8dprryE/Pz/cyeLFpUuXcP/99yM/P9+13MXgwYOx\nYcMGnDhxAi+99BKsVisGDRqE//mf/0FaWlqYU8yPadOmYevWrSgsLMTJkyexevXqmMqHq1ev4pe/\n/CUMBgPi4uLw3HPP4Y477ojJ38qHH36IrVu3QiwWA3CujD916tSYyItXXnkFBw4cQEtLC1JSUpCa\nmory8vJ+P/uN5kvMBR5CCCHhFVNNbYQQQsKPAg8hhBBeUeAhhBDCKwo8hBBCeEWBhxBCCK8o8BBC\nCOEVBR5CeFBVVYWysjLccsst+Mtf/hLu5BASVjG1ZA4h4fLHP/4REyZMwO7du8OdFELCjmo8hPCg\nrq4OhYWFAb/O4XCEIDWEhBcFHkJCbOHChTh69CjWrl2LcePG4c9//rOr2W3KlCnYuHGj69za2loU\nFRXh73//O6ZMmYInnngCgHNF7blz5+LWW29FaWkpjh07FqZPQ8jAUVMbISG2fft2zJ8/H6WlpXjg\ngQfw9ddfY9KkSRg6dCguXryIp556CiNGjMC0adNcr/nmm2+wb98+iEQiNDQ04Omnn8b//u//YvLk\nyfjyyy+xfPlyfPzxx1G9UCWJXlTjIYQn3csi3nrrrRg6dCgAYNiwYbjvvvvw9ddfu85jGAbLly9H\nQkICpFIp9u7di7vuuguTJ08GAEyaNAklJSX417/+xf+HICQIqMZDCM++++47/Pa3v0VFRQVsNhts\nNhtmzJjhcU7PPU3q6uqwb98+HD58GIAzgNntdkycOJHXdBMSLBR4COHZCy+8gPnz52Pbtm2Ii4vD\nq6++CoPB4HFO93bCgHMzrtLSUqxdu5bvpBISEtTURgjPTCYTlEol4uLicOrUKXz44Ycez/feqWTW\nrFk4dOgQvvjiC7Asi87OThw7dgwNDQ18JpuQoKHAQwgPetZgXnrpJbz55pu45ZZbsHnzZte2077O\nBZzNbps3b8bWrVsxadIkTJkyBW+//bZXgCIkUtBGcIQQQnhFNR5CCCG8osBDCCGEVxR4CCGE8IoC\nD/YZfroAAAArSURBVCGEEF5R4CGEEMIrCjyEEEJ4RYGHEEIIryjwEEII4RUFHkIIIbz6/8/eINLg\nx3nOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe2c88c8190\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "FEATURE = 'fare'\n",
        "feature = pd.Series(df_dfc[FEATURE].values, index=dfeval[FEATURE].values).sort_index()\n",
        "ax = sns.regplot(feature.index.values, feature.values, lowess=True)\n",
        "ax.set_ylabel('contribution')\n",
        "ax.set_xlabel(FEATURE)\n",
        "ax.set_xlim(0, 100)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lbpG72ULucz0"
      },
      "source": [
        "### 3. Permutation feature importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 401
        },
        "colab_type": "code",
        "id": "6esOw1VOucz0",
        "outputId": "b9f2d405-543d-4f69-8e63-65336ea63ca2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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7POO0bt1aERERkqS4uDj1799fkpSfn685c+bo2LFjCggIUEZGho4dO+aJzl69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\ndD75D8QlawAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe36e46b090\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def permutation_importances(est, X_eval, y_eval, metric, features):\n",
        "    \"\"\"Column by column, shuffle values and observe effect on eval set.\n",
        "\n",
        "    source: http://explained.ai/rf-importance/index.html\n",
        "    A similar approach can be done during training. See \"Drop-column importance\"\n",
        "    in the above article.\"\"\"\n",
        "    baseline = metric(est, X_eval, y_eval)\n",
        "    imp = []\n",
        "    for col in features:\n",
        "        save = X_eval[col].copy()\n",
        "        X_eval[col] = np.random.permutation(X_eval[col])\n",
        "        m = metric(est, X_eval, y_eval)\n",
        "        X_eval[col] = save\n",
        "        imp.append(baseline - m)\n",
        "    return np.array(imp)\n",
        "\n",
        "def accuracy_metric(est, X, y):\n",
        "    \"\"\"TensorFlow estimator accuracy.\"\"\"\n",
        "    eval_input_fn = make_input_fn(X,\n",
        "                                  y=y,\n",
        "                                  shuffle=False,\n",
        "                                  n_epochs=1)\n",
        "    return est.evaluate(input_fn=eval_input_fn)['accuracy']\n",
        "features = CATEGORICAL_COLUMNS + NUMERIC_COLUMNS\n",
        "importances = permutation_importances(est, dfeval, y_eval, accuracy_metric,\n",
        "                                      features)\n",
        "df_imp = pd.Series(importances, index=features)\n",
        "\n",
        "sorted_ix = df_imp.abs().sort_values().index\n",
        "ax = df_imp[sorted_ix][-5:].plot(kind='barh', color=sns_colors[2], figsize=(10, 6))\n",
        "ax.grid(False, axis='y')\n",
        "ax.set_title('Permutation feature importance')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "E236y3pVEzHg"
      },
      "source": [
        "# Visualizing model fitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TrcQ-839EzZ6"
      },
      "source": [
        "Lets first simulate/create training data using the following formula:\n",
        "\n",
        "\n",
        "$$z=x* e^{-x^2 - y^2}$$\n",
        "\n",
        "\n",
        "Where \\(z\\) is the dependent variable you are trying to predict and \\(x\\) and \\(y\\) are the features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "e8woaj81GGE9"
      },
      "outputs": [],
      "source": [
        "from numpy.random import uniform, seed\n",
        "from matplotlib.mlab import griddata\n",
        "\n",
        "# Create fake data\n",
        "seed(0)\n",
        "npts = 5000\n",
        "x = uniform(-2, 2, npts)\n",
        "y = uniform(-2, 2, npts)\n",
        "z = x*np.exp(-x**2 - y**2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "GRI3KHfLZsGP"
      },
      "outputs": [],
      "source": [
        "# Prep data for training.\n",
        "df = pd.DataFrame({'x': x, 'y': y, 'z': z})\n",
        "\n",
        "xi = np.linspace(-2.0, 2.0, 200),\n",
        "yi = np.linspace(-2.1, 2.1, 210),\n",
        "xi,yi = np.meshgrid(xi, yi)\n",
        "\n",
        "df_predict = pd.DataFrame({\n",
        "    'x' : xi.flatten(),\n",
        "    'y' : yi.flatten(),\n",
        "})\n",
        "predict_shape = xi.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "w0JnH4IhZuAb"
      },
      "outputs": [],
      "source": [
        "def plot_contour(x, y, z, **kwargs):\n",
        "  # Grid the data.\n",
        "  plt.figure(figsize=(10, 8))\n",
        "  # Contour the gridded data, plotting dots at the nonuniform data points.\n",
        "  CS = plt.contour(x, y, z, 15, linewidths=0.5, colors='k')\n",
        "  CS = plt.contourf(x, y, z, 15,\n",
        "                    vmax=abs(zi).max(), vmin=-abs(zi).max(), cmap='RdBu_r')\n",
        "  plt.colorbar()  # Draw colorbar.\n",
        "  # Plot data points.\n",
        "  plt.xlim(-2, 2)\n",
        "  plt.ylim(-2, 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KF7WsIcYGF_E"
      },
      "source": [
        "You can visualize the function. Redder colors correspond to larger function values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 512
        },
        "colab_type": "code",
        "id": "WrxuqaaXGFOK",
        "outputId": "39cab4a7-7872-4178-88d3-c6a699e2263a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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E0LH/0oUMHmJBsyjJMl2O8+C1D6Bs5sS++khlDJdeKyF2K4g7r9TuoyD68RAO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tnXq1G86LqYHnOLp+UJ9phjUFlagh8/ew/G6BjE33YvdEbxWit1hVDpAQLAqHZp8Pn8WDQt4G9i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\nK4JYpNOwXK9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k3eMhCRm4WIKUcOQkqq7Ejb4hL/aEY3hWN0mntyjQm2cCAOfy\nIJWKox1e0wqTmbeQo6wasV1fAtVDDa8BWqNMQqAxzZSJ1bIVLpx4/aPdcuu+/N5FeO2haabVHJnc\nyC2+kwZW4om/LMODi7+wHvOVJJSynRCd5RADtQC0laO1WxvQ3F788erJFtdzFAAYzo2sHZs2byWe\nn3Mx3p17Ie6dtwYbHC6DpFtNeq8Zm0FpaRn0mPvWNtSFvNi5vQFDArUIb23SmGXqTTL1IASoqqpS\nHkY4lsk/kAAQHT4InjI4WAabdrZi9MAKSBkGDo4zrS45OdaCPHFIm2ieGK8f3o4upFwecCWl4Ds7\nDDfp6ttngelTi5ffeBeXXxGAZ9wYTJ5TT41mkREUCRrbwji4ZiNGn34yJs+ut3bs1p0gucrkK/Fj\nxoJVeGLmuYb3jpdceMeZAitJKM2YV1b2djTjd/fPxVv/eBQNqzfYz9TTvy6KSLSErc0xbXhR6YOB\n9ZN2B0UeQz54DTu+/sSgP9PH5uhfTwkiHISBGO9COpHAU/eMRzoWgxg3n7aUIROuVz/Yhbvu6pU1\nvszZXmjMLz99EaRLd32rIlm0GXfm34shdiVHjEyDgHVEzBXeCaH2FMWo00qHdhzHLo7Jb9Xshlgo\nzkNu7dE0T2rjS6tJO4Du/wRkq0OpjICn7hmPVEbARytexrk1bsWrSd0+KQZujsH1107GtbPfRL+Q\nj2p+qbYoUGthZASqapDuaAKQ93i6Z/lXtvbPcE5IplNROnPH3H/L7Si5vbfgwxhQaa7HsIIkAVfc\nu6igB5Tc4pu+4COcNnoQejrCKGG7QMR8pZEQgqryrH7BX1EN0VutECcZV89ZgjVbGzBmWA1e+s1U\nVJUdmV6BEKAi6DJYFMjasUVzJ4BjGVw3dyVm/fxWqt5M3VbdcaAJGYexOiUgW4HqUTcA3kCJUnGi\n2V7QMKg6hMlXXQk3xyLJC4ilM8oABgHAOZwgOXuE0QMrFNE4x7HI0CpPrANOB4e0VVXKcF3lTRJ7\nzngA7ssnY/ATixWNjdwCanr8YQQGD8HNCz6G1ymgM5YCCLBozsX00Nscxpb5ECUiXpo4Ffv/vc6W\ncDx7WHmh9oRn/4R9n67FEzPPpb6XgOBC0YXDbhFRTl/9kdDmELE1ICDOAr1efA/Lxl1aUH9VCIXa\nfR2MhFLB4vdH5yQOGCft2iUBFQ6OPmlXYAKvKZVGtdsJxhuA0+vLWsp4fWC8AWTJcsD0vWI8io4d\nWzD5ouH5iTrAWE3SESeqximdAC+Iptopg85J1QIs6P0ki8SFDPhYNusx0REGq79dmGgOj+PYwnf+\nW5Sr1PLT9B0TS7D7wCFNS00tFA+4HIiqdE96mPk/ydtxObK6E5eTRZ+e1bhh9iuoDrpxbt9yjell\nsXCUVGHJbyaZml/SCJMaZbWDMLxflbKuvUDg7kHtZbTwlnFoK7INSpuus9M6U1eNmNI6RMpOhhis\nQwkXR6nUhGBpCVY8dhd2vPmH7GSewwPRZwzsDAV9ilD8rBP6Yv3fihe6y6gaPBoLbxmHd+deiDED\nQpg2T2tRIJPLz7e34IUHJmBv1Py7kUnv5nQQTl/+ZqO3pKgbOgoOV/b6pIVR0yD/Dah1g7IAHshe\nM3wmDUmSFA8aWTROqzzJollu0KmmlSeOZSCw2hai2iQxVF2NSKQTk+fU5zU2kgQ+kh3hlqtQmUQK\nJb6shQJHJHwz839ouwMAVBIWHWz2g730/akFHbtl6HVOK2+eafleAoKLRTciIS++CQjYpvxPRIoj\nuKK8F84iLhCQvP6q3lp/VQhWDuSNrIjzygMGAbgzVG5uV6CbtJNgMllpAw2JFEac0B+M1wdeyEVN\n5aqOdhzFt//tEZR985mmZQcYzS8V6DVOKhLUEulCBWVqD4BGx5RJ8xBqRmttCUzafXqRuEy0Orav\nQbXqnNN80o7j2MR3kjzJf9/q2BYg29prbA1jy74Gzfp6obhsQwBAQ5RoRpim20lkEKrsgZlXDkIy\nzaMq4LL19K+H7DQOACu2NlmaX1rBU1qBYHVt4RVNQFgWkmhOztQo5GVkuR8Lp247uHrOEoy/81lc\nNXsJKkt9kJwB8JUngu8xFuWhKpx1ymBTo0sZahLGC2LB9a2gnIu5K8GxDBbN1VoUyJU6mURZVQNl\n0ls5cBQYVmuKqXebV0Nte2EG+dq10g2ymTjYRLvBg4ZhdPEsqqd3b0U10ibqAOmUS+Ede17upsmA\n8QY0JolSOo1Mhsey+RMR3ZGLAVGJxgFgxx034MBfXkB47QZTc0w1vIRBmuTFknbDgA2VnaZW6/cS\ngh+//jxe3rsRC1/7By4WPbhYcGOC6MEfVizDz775GD/85/PwVoUUUlZ7xsm4sn7J/2Pvy+OjqO/3\nn5mdvbJ3bkhISAg3hCABAeVQEYgWBTw5PFrvo7ZW69UCAm09qm21YrXqV/GAWltFe+BRK14V5EYE\ngXCTg9zZZO/dmd8fszOZ4zPHBmzl9+J5vXiR7Jy72d155nk/7+etTaCMktF1nk+HhcO0l/7YQ5Bo\nWiRMVa8+p5vMDqTns+m9QAZoiMYx5Nofw3/NvWBjEVjAIXGsFgBnmPsEAAfbghhZNZi8czMeJ8k6\nzZ3dyPORjwOkiU/dDlhtDDExXAWNdPFYTgUamHwEBvBle62ctNM4NXFK//VI3zFSwkTqwPN4vHCk\n1B82UmCmkijpzb8TiJSwHwpA/9J+2LR9r9gyLtz9m4Wy5EJ8DaAOwjwRUAACkrKR8Lu9oBxc1Fxo\nm16WUd7ASt1tSUndZmbGAfz74fVlC/Dhk9dj56t3ygkYRaOlO2G6e05IF/9ip7n1tc5R+lps2Nts\nONdO+fobwcw8QylZrz+wF19+9jFxX43dMd2cNA4c32mnyKCx0DRSUu+S5O7dEu1GjDS/Lq0M/PWj\n/XCWVMB95Q959eGKHyP0xtNo+8MiBJ9+AB3vvY3gN7vgGThYlRsktMJ7QOHTBddphmOqn4cGDMjJ\nm7O/j5cnze6VUiV4pZSPg+Nw7MvtYtBs8dhR5OBOjcBOM2DBwZrlRN64M3rynAaUiYQpu2ok2rZ9\nJSefipiCJi6FfDMTEzRyoMKMFfnDqjBvyXuwOZ1iVIFeorgUhzu6UVaQWdq6lip1vKMbxZVj9AlR\nPKI9346wnVbX3X1Pvoc+peU8UVKkh5/Ocjq1cUoaxoEe83c0nhS/7KW+ptXLaxAjdOB5PB7U19cT\n9yklQ1pEiTSJvtjrUBnMPQ4rblyxFePzutDUFcW6Q+1wMDTGFflw0dAClXlX6IaSIppkEUk/h1As\nqVouqA4VeW7UNnfjhQ1H4FIYxTOB0NFV1c+PbUc7cP+anXhI/H0w7luzEw2HdJKFJZCayDOB1ky5\niSNL8J+vjuDyRa9petukxGv18hrc/MiHeOruc2Sdb8pOOC0IZUIz6wtq2cSRJdi0uw6z73+ZP8dE\nCKnOOvG1aOuO6apwFCV//e9bs9PQ5ms0z3ByaQB5LhsYhkZDZxQvfx2G1aX+8hfew7FEkkieKFCa\n52KhabDECwEFh9eHWCJ9ThLzrqAMXHLOAESOHYKzuAzzlvB/N+rKH6L7T08C4EBxHDyDBvd02Ely\ng4SOr1KaweFUHB4dxYk/Hd7X42nrQJBm4WVp2bK5b7+E0rOqcezL7eruO4rC3DUvovSsas1uNim0\nPEjKxwHgjQsW4LK1r2HE2FHEYb4CmdIzhOuhjmGRF4zJDODK7jqZWTxdxpN2Lh7mkqgeoS5xK1/f\novt/Ce/Q4WLXnfAasdGojCQlWxrEzUJvPI1YboHsMSXiKVaWCG4aBFXqaEsHxlWejVTffj2mcAJU\nnXRQm8mF3xEKwnJki6rrbt6UQuTl5IhEiYkHkYCX71aFV5WXdhqnDk5J5YmiKKKqpIwsiBCmw3t8\nPgT1RgtIoDW+RcsvJS3lBaMJrP7FhYDFinWHekgSybyrHAIswMHQcNoY3PzIh3DaGFW5T6k63EgY\nFpwJAllWnFESwMIl7+KMkoDhwGAtKLvKpIjv/ZfhHVcmM+WkkBKvls4wnrr7HJGACapQprEDZtbX\nyqLyO1OgGIc4DNhopl22296r11trnqGgXNI0hfmL1qKP14ESNw27Q65iCu/hKx54G5FQV8alUpqm\nNMp2a+Hy5yDK0UTzLrX5b4hs/Aih13+HyLFDovriKCoTyzdsLKoiS8qhs2PPKMIRzuCGQeLrufOF\nJ3HYysqW5Qyt0C2fGYVXkqDyIKWVLSGkEwCvJL39Et64YKHmMF+pSd3Q4E5Qz44xKXwvx6sygMt+\nl/ibSAOBG9kkipz65XelKijkQEVSKTgstMbYFXO5TydzOG4wlkRO337mSnIKxUlpJpeX6+RahL1p\nNzoP70JulpSk06AYK1IsB4qxni7dncI4Jf9yHMdp5jpFFYRJ+ZnLzy9A0/Hjpo9llNWkVKgK3XYM\nyuW/+Pe2dOP8y/kvQ0FZUpp39bqhokkWTV0xccTGJ/94U3ZsqepwsDWEslzysGCz4ACZmbMjoh2c\nqQWpWZxEEmhfEQJ+p2obaclLSljMhE5KIRCvEQt/i6prnsTli147IQ+VGQhZVMKw6LFDi5Drc4EN\ntYJ28Rcykg9MGZAZbd6GDzbt1n29SWVaabMAx3Go3c4rGsL7TZiB1xCMIhQKw+6Uv/7Ce3jZNUPw\n8Sef6natkmCxWETzLwBZ2c4GFpFYXNO8yyV4gh16/QlEj+5HZXm2rHxDUxQO//IBGVlSdnY5LRbE\nDE5aSghGXjADUVf6dU8TlKs/XYNwMKRZPjPqZiNC6kGSlt3efgkA5GQsN6A5zNesSZ1U2kuAA81R\nsKQ7AOJt7T3lOI1RNqSBwCmA34cWKAoFt/4UFG3By0tmyHKg9nRFMMidBdLYFWmDgJbnKZFiYTU7\nWBcgh2RKl+kM8dWFcjtJaY9oLgcAllWZ7BkLjYVpD+RpnLo4Zct2etD7HrVarZhRc+FJPZ40WHNQ\nrhvzF6/FqmU1aEw/pszZkZbo9LqhJpcGkO+xo6mL366rvRWeZAI003OnJM10uuHMEs3yDQAkIiEU\n4ziOoYD4PNrDCeyo60RVPz921HWiPZxQBWcW9vejUad0JyUJrzw4U6VAWfIGIb77n4CND46Ulry0\nynLK0hlFQbOUpiReeX556GWuz4WWzpCp0l0mUGVRtbTCZ+uJE1D6wAQlSgjIvPHZ9Yi31eOxz5vx\nwvagJnFaNK0UnmQ7svoMwPPpWAop4qEgGg/XomJUNQCI7zeAf69FI2E4HOqYg2PBKA42degnL3OU\n6N2Qgs7ph2SHvAlDKHn4ww0IhUIy8y4xxBAcuv/0JMKK0MQ8mxUtsTgKTarFWlASAoSiiIOBPy9H\nVg6r27QDI6qG9xCkdNnMTHilCMk2AqRE6LUl0wGOMyRjJJO6HpTHyMrLxpb24xiQsIjnpSzHaX1Z\nSst43RyLLAOFRAgwnbd4LV9eXfGouGxvdxjXzawmbmfG83SovQv9A9pRBlIIoZmJlnqV10lYxn1+\ngFiSMwPldvam3TwhK6pUBWiST1A+H5LRUuEJn7PT+G7hlCRP0rLd6uU14mR2LdIktF0LKC0rQ12X\ndso4CYJhXAvCMqVPiqEpUVl6bVmNjDgJPysJlbBMuV1ecX80HT8Kf1HP8E5BdaAAvL6tXva7OijT\ngeO7NwNDL9B8Hvet2YlAllW8KGcaa2A0+JYSht6mR3zk+tTkRisEEzAmW0pidSIeqkwgZFEJx/Y7\nErDkjZKtI/WB5XjUJDPI9YxHIcGfZYWf7cTdv/kH1r70gOrvCwDRrnbkBXLlj0neV9FIBM4s8uy8\nSDQKl92m+rwAgI2hEXF4QLlzxGRxAVarFZEEoWyWSsDjykJ3JKYIKOyBhaYkuTtSZYIPyix02NAY\n5TOC9OClaAQ5Fl6di7yUEJQlLDhoTWGwgqDICJLEByUsM5sHpfRGkZQrM2TMcB0JUVMeI9TUgmYH\niytzeAO3VH0Tg0Tb2smBohJV6hs2jqG0fglZGWAqVQajKRZOq/alJvTGCt2k8f1tXaieeIbu8fkn\nqAjNlHrs0ssuv/9tTO2Xr09wjKDcTmEuNyJkTDwILkFrEqekzSsm+EOjeeM0/vc4JcmTsmxnt6jN\n4wJIxvJMQTKEa0FpKE+yHFFZUqpRSjP4uCIfKAp4eckMcbuiAYMR/3obAPnkc5Jx/DrJ74JCQdEW\ncKzxa3Dv9MGapuVI3R5wbA4onc4bI7O4JW8QAhyL9mCCSG7y/NqqkB7Z0iJWUuVKUKKuXvoeXl5C\nJmu9hZTksaEWWMvOUi0XXhMlyWwJRmAHpWsY7wgncPhoPX79kwsRiibQTVAXy9wAmyJMvU9j+sVz\nQdPk4dkeOgW3Mws+p13ViGFjLIiCEm9YuETPnbHNZkMniTwB8Lic6I6m3wcExSnb6UBrJIYCt7TU\nkh7HUVKB6urP8fYD92o+HwElNIMjbAIjLDq+HAkhmJbvxYqONgxOqAmK8D9JyTEiT3rbqIiQmagE\n0joCYWpuUxE16TGOMiz6JXv+1qpyXFu7KSXqKJfCxdNGgQ3qd9sqU8b50zdzZ6Iu50lxrLMbl+Sb\nyMeTxhO0NcqJenrZkoUVOPh1qPfESQMkJSsSjcFh1yD9OoqTEGewenkNqAhJpT2N7wJO2aKr4G2K\nsynNocBmBgYbgaEpZFktYDkOWVaLZrq4FEqFqj2akOXsCKrSrY9+SMx9EpbPX7QWjIXGl3X8l1Z2\nYRHaGuXlEUBtHM/32DXb121ZHqQi5C8qCjA2iXMsvDim+/ylJAGAyttjyRsILtQk+pwEn5IZf5Jy\nfIqU+GiZy0keqpWLZ8BC03j67tnfig/KWnGO4TpCttMNz6wH190EX2GR6rWXRhf4s6xoPl6PJ946\nAJfDSvS1hYMdyPJqB/AxjBW0hroVj8fg9biIn5dUipUFZEq//K1WBvEE+WJEURRZ2Uv7UvLdDjR3\nyy8QUh9M/zPGo50wzFYJU6ZxBdwshSDNapKY3vicdLfJIFdKExJf02X/fE3um8rPkZGzw0wKF+fI\n3wtSkzjJGK4ECyD3whkYvOJlMeVdE4SU8YZoHH0MVEMjJFkOVovxewDgmxASrY2wZhfKk8XTy/b9\n7UUMzbPrm8R7CwUhO3a8GUUFuRora0BR1juZRvnTOLk4JcmT3cG/8e0WC7wOO+KJFNE8rjcwuMhj\n/gNtZXiDn7WX41U6O+TKkhBB8My95yESV0cQSH1QDRK1ijceqj9Mynb1xq6YZvt6/uDRyI+pgzaF\nmIJn55+BYCShaVp29B2MaP1e08+dZCCnaRovPXovtq38Ed74xQIAPKkx21mnHJ8ifJ+bNZff9vga\nUBQwf/Fa3eOYzZdSIm9gJWiHcYKwlGS66SawuRUyg35HOIGHZ4/An647E4/MHoGOcAL7jtTjuSVz\nNH1toWAHXF5t5UkPDocTDqdb9XnhOCCRTMJmY9IlO7kCYbPZ+IRxjQuS8nrLVV8sdt4NqBqO4wry\nJPXBxOsOIhU1vvt2WiyIZ3ihuTzgwy6bPuHSS+1WQdpNZ2Ybo9BLAqTKVvG4UTi2cbtI1ISk8rnv\nrEQdw6JvilYngkvUN5UPjIAjbgfOurBG1UFnFnu6w5g4TiPc0iQyurexOWHNKSQ2JwDAofrj6Dt6\nqiwN/NvC0YYmFA0zUW5UgIkH00G0p2MMvss4NcmTnQFN96hKNqsFwag63E8o2cVOoGSXZDl0RhJY\ntbwGnZGEYfcdCe+8/qrivPgIgnmL1hIjCADtVOhJsxeo1qUI/2u1r/v69kdn3QFVGKN08KzXacVN\nq7YQ064pmgYo+oTSxqWPScmLWfIjHZ8i3Z6ieGI0+toncetjazTJT1O78XHMJp33lmAJ2wqKnGfY\nJDDeXNncQenfRFCittUHiX9XAX3LB8Hl7d0IoHHjJyC/qJgYlBlLpoBoF/EL3Wa1IuzMNXVB4sZe\nDGtuH/Hi1qcgX0WeAMja2jl+LggxfNE0FKGPAOCkaFg4ChFK5zNtVi1SdNOFW9r1iVEvQy+VylYq\nzt/cWKxWmQp1xMtgTrYx0dnx/Vvw2RTeA6kazwJgW3sLqvJziV4mrecl/TsdC8dQGtBO81ZsrJhx\nRyFmdcJmUnUCoJssDgBJiw1Of446puBkKFHSfVisOFx/HCXFxb3b12mz+Hcep6TnKRZLgmXlqhKr\neK8pAzOjqZRMeXrzjT/jzJmzARibwUnBmEaQ7VNxR2x23hjpcafbA7TKCZVQtluweC1eXVYjGolJ\nygRF0ygbPx3Xnyf31kgHz35d34lDrWHN55ZVOhyOxDF0ob/Bq6BtIJc+JiUvZkIpSSRL6nfq6I7C\n73aI///nqyO4YvFryPH2dNpJj0Pq3h8NYXkAACAASURBVDNjZCd5rHIr9BPUpdtKu+0Wv/sNOMgN\n+tK/iaAC9htxpm4Iar9Bw00dXw+aAo7GArvdgSQsxt1GNies2YXYUdvClyVaGuCzAl0x5bq8WVz0\nwVAU+t73C/iGjVCFL0rhpGiEOBYupWlcp8vsCr8Pf+7oRHXMKls/kxBKQO11uuyfr6I4HXpJCtTs\njZ9KgOBrAvicKGEfggr19/97BXldcdA55MYAGdIDDFVG8tY2JDkOLIDmxx5Eu8LLRAQhJJMDHzdh\nDBruK38IR1EZIkdqEXrjabguuxUHGtswumgUAPMqjFZzAjdmFuivu5GIJ2XmbmXwZW8g3QcAMC4v\nmpLvITtwAoT/NL7TOCWVp1h6YK8y00kKvZIdADQ28mm2eoN+pciEOCn3yVitSCbk3X1m5o1poVoR\npmmUMq1EYXEJqtJhmFJf0/1rdmJnfRDD+/rwyOwRmnK5o+9gROv2mD5fqbdH7zHAfIilNEgTkJOd\nXF8Wbn/sI/H/iSNL8Navrsa2lXeIY1v+vHwBWjpDmgqTGRUskwBPJZSKnFYgplSJAoCc/kNMH0MP\nZrx7Smj5L+w2K8LBDs3cHHGztCpQOSAHidYGUJveJgyapWSDYuksLwIeNxL5hYaloxKKwVE2qVKZ\n9Lw9XopGkuKQEMrhJ0EROvbldhSPHaUbqNmr3CgBaTVMuY83ahbgmaGTsfKhRzHXhOokgJTrBAC7\n2TiG0Tail4kEIa5A+DtFnS5kmcoyouC64g44+g3AjgNtcJZUgMktgLOkAj999M+onlajn91EfFIK\nNdPmRMTmxtuf18FqY2Cp2yGavElz6TKCbB8+8WfaauvpLj6N/+9wSipPUujZHKLJFGIKxUm6oTQd\nfNWymozVJRJI+6wYMhz21kNIFQ5SnN/Jk2aleU9GEMmWwtfky7JiRF8vFi55F68unSnLd5KComl4\nK8+D0TQMQDttXOr3yS0fgpYD35h4lvLtpYSGlC4u/L9xdx3GDi3C7Y99hGfuPQ/zFq0V1SQAmgqT\nkQqmIlgtrcj1FYKyGZMoqSK3/ZjaW0YB4usvLKsqz8wfI4UQhbH6hT/gp3feadg9qowrUJOcHtgd\ndiRbj8rHU2hASxUQIJjF5z/4Hl5ePAPOW5ahyv8atn/4AVY9OE+3dDRmdF+8vfUoLleoTFrkQMCQ\nOINvbEmMjFtPiiIUbmrF3HdWGhIj07lRJo8JioL31nn44ahhGO706uY4KSEbz5LGN2wCt585oGcl\nigKjo0Ap4wo+P3QU47KNvX90lhvO4v49VYKj+5FsaUDkSC1mjS+EKxkmD//NBPEItn72ER6++zJe\ncRL2pwi+zDT3Sb0P3g+4etlMPPbwFnX5zSi/6TuU73Q8lEA8dfLOxWahkePJkAR/h3FKkie7w4pI\nqqeNWu/7QWuZ3eFAdziMYNROHPQLGJfzSCDNxBs2ajTWvvlnDFaQp5MJacq0GTy/4QgONcrDGDvC\nCQQjCaxeXoP2UBwdOvlOVm8uCr3QDcxUlqZufHY98e+RPLoJ2Tl90Naq3a5sBMHvxHEQS3PSMMw3\nfrFAJFRKNUlLYTKjgkkJlg/NGZ2zEOlgzVEkriPzGXd6kMZifMWmDG8YtOI9OI4jfrnbrFYkkknz\n7d/KcorkZ8EsvurBGQBFY97itXjqjpl44eorUPLpu7oKiM/KIOKwEUtQJHIgYGo6toAFOY/JNCT+\nKFPEiOMQbm5DVn6O/np6ZUTJMW25AezvbME/Poph1ZLRms9XmPEnW6ZIG+/kWHgoqqfkpjO3Tgpp\nXMGRcBTzZozRfl5psOFufjzP0hmIHDmA0OtP8Ofw+u8R23YQVKUxATODnR+8hR/cdIOqNKeKGcg0\nA8piVe2jIxxDgFL4rST5TSTvoNHy0/hu4ZQs29ntfBu1g7HA57TDwWQujZYPqMDRgwf4Xwg31WbL\neSQoZ+K53B74snnpnmQO1wNpfY7jkEpkFvKp2gd4sqQ0jXudVtz8yIfwOq2mZ6tpgWQWJ4EpnYDk\n0Y2ASRO6EkLpbetLd+Dpu3kfm5L0KMe2COU+6TLpY2YhEKycgnzAmgXK5lJFM+ht29LSglSkW/a4\ncsZgfzPeFQmk7xlp2GqO0wKLhSEOvAaA440NmvEeFE0jyThBuXOQtMkvZppxBGkwFhqJlH7DhrQk\nKJjFI4f3YvXSGcgKtaMtGFQTJ4KJPBGO4PiGTWqVSWMUiYCBCQZ7bPw5ZtRhp/2ETAdq6pYITa6T\nlZ+Dz4PHcf6IM+TPXWmUl8z4U5rDpfg8FcGc0aXi71pz60jPO9nZga5EEm5T38vpTK/i/ojWHRSJ\nEwDsburA8MLeq61KhGNxw7FVsfyhGXXiydaXEK7de2sxbEDP6yfNb7La7eqZdkbLT+M7h1PyLxRL\nKywnkuE0cNAgBOsO8Hfhi+RDfbWG/WYC5d38+d+bozkAWAta64e7gojt+CDjc5KCAvDE5ZViGzyF\nHoPyip+eqzvLTtmpJ1smIQ5GaeM921CwDjwf3qQ6w8oMSN4jpZcJ4EkOSU3KdFiwChyHxMFPYC2f\nbDjbTwlHcBuU8RPKGYPPzD9D14MmIBWPwt20V/aekTYn7Nx/BIHcfM2B12/99S+aXkGaokBb+CHV\nml/uGn4Rr8eDrojkb6/wr/gcNnRGpe81PjQx9MbT6Fj5CEJvrIBFMcxYUEIGPrVSlj80hLbhjWtv\nkA3ANYPpeV600SzvfToZeUx6SJMdM8OGhXUWLtNYJ02uhj33S5z745sRWfSLnudOIEpK/5erolx1\nzDjHIcpx8NsY8RgFt/4USY7C6mU1prruPm/txEWTRhi+FNJML+lAaIDCV9kVmLbsKVVe08mGSIAK\nhmXmf9LxS31dewiDx0/tWVeR36QqzRktP43vHE5N8hRNGBrCjdC3qBhTzjmPeBdOKr1pwSyxYmgK\nfTQGAJOgF6Tp8voQ7tJP+zWC287gnZV/UJnGlQZlJYSSkkC6+vTvufOXEoeXbpsIigJu+uN6XPr4\nxypjuBJ0VgCWnDL4HfqKGikagGTuPhEzd6bw28Ng+o0FRVtMq21AWkGMdMPilM/tEmYMCli45F0U\nOxII7t+uex5sZxO4SFD1HhOaE2oPHUFeYR8A5AYIQf3RasRIJPlssngiqfpyT7rIUQWx/KHIHns+\n2kvH88cYM0vMeBJQ6HaisVvZ3UnBddmt8F9zL7y3/goFF12KvJ8uFUmS0pwsKCHnjO2H3am4rsqk\nhZExBlvtvVM/TUOiJM18/jem59utXDQDlIXGzOd/I1OLsvKywZT1wQO/XoUb7vkJbAG/+NxJRnmp\n/ysS7MaZa1apFKgvUlGMt/SQVUF1umbZe+DYlGxunRYaonH08xtHFGjNtqOz3Igydtzw6GfEvKZM\n0d4dhs9F2IeUAGV5kAx3mR8YrDNguD3YhYBfrooa5Tedznc6tXBKkicBet12yqRxJWiahs/v17wL\n13pcikxKewIhM4onEGAUpOnLzUe4PTOPjRRdsSRolx+P3lQpU5mMZtkps4dcbAhc+kIqEIddB1tR\nXZGDF2+biGdvHI+/3DVFJFN6YApHgIt0ILe0grhcL3tJWXozmxl1wmAT4MLtsGSXAVCrbXoIONrg\nKBxAXHbfmp2Y98IGdMf4G4Qt279CHPplEE+iHe68IuJ7LJpk0dRYj8K+RaaelvJmhKIpMBZLOleN\nkStPFA2LzUHMzmFcXjz790PoZC2AOyDOHpNeEAeOHo7GLnLK+M2//gg2lwvrvknhKMuJJElrlpqF\nouCgKHST7txpGq6B5NcbACYXeOFnKRxmvr15Ykq16d0b7jIsEb57w12wUBzmL35XpT4Fm1ux4vdP\n4cO/PolIsBvxtp7uXS2j/I7v34INs+fD6XWrOhBTHIfjXApjxqbzidKqE0VbsHLxDFOqU1s8gYBN\nWR5TZjj1QJrpJaCpuRl+C6XOa+olidp5pBEjSgrVCxQEyN6yD5YjW0xHFtibdhPXp7R0YiNFSbFc\nr1HjNP63OKXJE6DsCOL/l3qhSL4o5fuxNx12mZb2GJrCsWBUFU+gpUAZBWkOGz8FzDFtJYIC4CHU\n96WPH80eiZ/8bqWmykSCMnuoubEe7jifON7aFcO2g22orMjFvEVrUVWWLSdTt2oTKKHcZxs0DZQt\nC3kD1XlJemoSqfR2Il4ms8gbPAbWITWyx258dj2mLePLqnrlu9DBrcgqr9Lct9fZ40E7fLAWffuT\nSaWA1oajOAKvZgTGOTWzUFjUT3N73VEQHJBI8kn+8URK/iXPsUjFo+o78PSF6bd316DxwF6gu50Y\nYFiU40NDl1x5EhSJZ356DuLxJJ77xbXYuH4DkpL5anUP/Qz7br8Gdb96QLbtrFH9sCGluOmhaUz4\n/AOM//vrmPDFh4DGiJrLc/yoY1Lopk5C2YSQIK4ypB9vMSwRho+3EBWqFDh8GaBxx49+hB8/8Tmc\nXrdqxIp0HIsIjkNo334isdrExlAtmQ8oKHzzFq8FQ3Fq1YngO/u0pRMXT5F+fuXxE2qTqXq23ccH\nGzC9kEPyo5dBbf4bvxZBtVRBg1ztPnocVedMIy4TCBAAXj3N0f+cqaBQqBLJJCymIhoIkNyUJG1e\n+PxOcaLGaXy3cMqTJwECSXKmO4UEL5TSF6UkU1pjWoxUpUxKe9J9SdUAPQ+UUZCmLycfwdYm4vGE\nQcE/P38QbjizRJY8Ln08Ny8Pne2tGc9Pun/NTjGB3Nl3IGKNteLA4e8//R9sqm3llZd9LSoyJZSx\npN4osz6hTNWkE/Yy6YCigKFjxiLHY1fdHaZzB3XLdxzHAmwKNKN+/ylH5az46bk40tiKmEVf4YxH\nIrBnuWTvFSnppiiq13eytIWC1ULj5kc+hM1qART5NdZwC/EO3N60G4XxBrRs+5g/h81/k10QASDg\nzkJnVF2uDb2xAh0rH4GNoXH7775EMNRtajxIgcOGNo6Vva9dA8qQle3DvEVrkZXtg6uiXJU4LuA6\nXwCbHMme7KfeQMfo/ebF1+LZYVPw7vU/Mb07pYk9BQ6fOxMY0ZGE9WgD/nDXVGIMg55RXkasKApM\ndgAH2QTOGttDsLUUPuE5Cr6z4sWPis+xNZ6QDXqW+pqcJRUSX5M2GoJhFOf6ZYoTSbWUPVUdchWK\nxuFxaTRepJXSE857SqP2SD0G9DOn8EqRtHl7GjIkBnK7gcn9NP43+P+CPEk7hOw2BtFEjxdK6osC\nzJnMtVQlpbpkprQn3VdXaxMibTzhkXZBaXmgjII0+5YPBptSezSUg4KFDhPh8auWrEV5dhYWnT8I\nCy88F8Up80ZtCsBD6Qu7YGJ2DzkLrgivXnEcT6CEAEwZmUqbxpVkKcdD9gmR1Kf/hppkBIoC3nrs\nRnywaBrWLZ2O5wmET1m+a+uOyTrwCoqz4Bl6NnH/pFE5W+qMQwqVJDjTBoXCtB+KBJYF4lLPE0so\nbaUSxItOwOVAR0Ryd04IMCSDE/N+Vi+dgXhLCxId6c+ChmFcwEDain1czzFD+/Yj3NaJ1ctrEG7r\nxMAl92l2nNkpCtd6ffiPM4GUFoEymEtnZAaXzqEz1e0iMbELxGlUjMHkfC9ZXSKMoyHtU+jIq3zx\nD+i6/4e4fNHPVeejpfAJqtTuI53wDhmG4p8/goMUg3KXnORr+Zq0EIon4FSW/QzGruiRK47jNG8Q\nRbN4ToWmfylTfLP/MAaPTX+2zXbNKbvtANFAHssgguY0/nv4/4I8Sc3j8UQKDmvPPLtoMiXOvSOZ\nzEkfKpKqpKVEGZX8pPtKUBbUbfkEQNqD0sUrS01dxiNaSOSqctI0jCvPUz2ulTguPP7K0howDI0F\ni9fiyotmgk5oj2KRggJQmpOlmrfmKChDvLUeXJLvqJIGYCrJFKCOMOA4aHbl5Q2slNVmT5aadCIz\n6QaPHouxFbmgaQrzF69F9UCyMVxIUb/pj+vx4q0T5cORrQ7Ycsh3p8rSaO2ho3D6jaezD58wVfzZ\nDDlXYu5ll2sv5DhYEyFwoTZYo2Qyr9XmnSgcDpRWEhUBQS2wDh0LYmYIejwxvi3/wbF0156WYVzA\n9LEl2J6Sq1lfnHU+1s+6ApEDB5FTXUVMHBfgpywYHWXwOYlAkVQlBZnSy4sy02WnhTg4fOpMoCrK\nYEp+OjJCqS6ZjCMQYMsOwD96JH759FuYdvV8tbqnkTCeDHYiFopgWFk2dtS2wDlwCHaNmoAFK56F\n8m9J8jVpYf2RJkzol696nKRaitAhV9sO1mNk/77qbRTdcvbWWm2/UwZK1IFjDehfWoKkzUeM9iCC\n0G3HxIPo7IiIEzVO47uF/y/IEwCRJNmsFlGBEsp0XkdPmU5pMn9mxe9V+xL8SXtbutHYHTuh6ALp\nvsKMC8H0l9Dk0gDyPQ5E40nkexy66oCRgkDyNmkNBhYe39vEk6uDbRHkDJ9g+DyUpSRVOnnV+XC0\nfUncVkqmAHKEgda4FgDwJg4jp6iXAzZJz8Xk0F8Scgry0XTsADbWtoBlOaxaVoON+8gxDBwHtHXH\n8H+3TER1RQ52HWw17MATIO16rBpSgpLqcwy3KRrQM72eJ+cx0w0KRuA4ji/5CYqT4o6ao2hy2SN9\ngfrrulp1uUWiFuSWliFIaV2geE/MlAlDsTPIE2fdchJ447iXotHOSRQylkWitR3ZVSPF+Xpt277S\nLGtNLvCiMsbgC0cCrIRAqchPfg6xRKeVF0UkVgZKFgA0W1h84Uzgeq8fkwu0L8h642hUSJ/r//36\nMbzw2F3mhv+mwXh9sLucmLdoLSorchEJdeOdz4/CVzEsXZqTmsTVviYtfNPcifHnTiQv1Eka1yJX\nH+/cj/Nnz1FvQOqWIyhOmWY/pVgWnDMAq92WUW4Tk+hWddtlaqk4jf8eTknypOXbUA4LBshlOun7\n0ULTSEgCJ6UKU6HbjkG5bhS67ab9TVJI9yVsk+Vyg4uFxRiCLDujqQ44GNpQQZhcGlB5mwDtxHHh\ncSW5Mhr9QSolSY3mVm8uvJXnolASXUDTQEUfcoeNkiwpCZYUtiE1SBz4BNmBzMIitaA0nuf5XaZU\nqOy8ABJHNoD29sGNz67H+cv/halL3teNYch221FVli1eYLYdbFMlipMg7Xpk7A5Ynfrnp5x3yJNz\nO5q6omLZN2UQVKkHluNApW8aZN4McQWWXPZIX6AumTpAXW6RqAWlPhf219XrnkMfbxaaJEOEtcpJ\nAmaPLsGGlOQ9lf4CaNm4FSP7B9C6aRu2Lbhe95hT870YGmew3pEAlyZQSvIDAMUTx4KjaRRPHNtD\ngnTyomTEyiAIkwWHrfYEGiwp/NAbgJuidctyxC470vpphWrIm6+gpb0dzNOPaL6WJMgJ7C68+tQT\n+NNT96ZLc90GJnGNfbIsr8z2IlsPgIpcpVgWSdCw28jEXKtbTkSGs+8isThsNhusdnvPAOxY3LDL\nTvxMWY39YKfx3cApSZ48XodmqrhUWTKTBVVeUYEjB/cDUHudvM6enxu7Y4b+JilIahVDUxh95kQE\n921FU1cMT99zHkKxJFEdENSmcUU+TeO4QKyU3qZvA8pS0uHWsCoo0+LoucDTNLDuwelYc89UfLJs\nuqq5SY8sKUFZrLANvxhs13H4kkeBlLntiPtKfydLjecr7pqtq0JRFFBa6EKyfgdsw2eBomg+HTwY\nM3wOUpVtU20rrl3xn16fu+ZzgnaqeL6nh3Cvev7pEzwOpZuEbDu+q2fgqgT2pt1gD+0gllsEtWBI\nsg7727qg19IuQLwbNxhY67cx6BKM45JSFgB8NuUCbeKkIBrn5XtRkZArUDLyw3FgGAoLl7wLhjGY\nF9XzJERilZWfo1nG66BZfOpMoDRhwdXZAX5ciomynNIMTlpfUKhmX7cIty8ij1sxQt3DP8eB++/A\noaU/xcF/roHznWcQeuOpXpnEAWB7QxtG9UmTz95mO0m220j1w/jLrtNXjfT8TTpZTiSs3/Y1xlcO\nQSIWQ2VFbnrUikEmn0G6+Om4gu8mTknydMfj63QN39LvAL0sKACoGDgIXfWHAKi9TsGIXG3KJNJA\nua8+Hl7FmjJ2NJoP7RNVgb/taSbGF0jVpi/rOonGcWlHntTbZAQawE0TSvHz8wfh5gml4j2hkfok\nlJLuW7NTFpSp/DMU9vejvMCDbA//hZDtsaO8QP+CqBxpov6dgrXsLDBlk+BNNSK3bIip56o8hlCu\nA4DR1z6JWx9boxumSbFxzB+Zwn0XlOLlJ5eD1mhx14Ogsl274j/gFF1gRjD6mwgdlFqp4iejZAdI\nCQs5Cdlht6HTW4ZUUSXxQuW20vKUcSniERT43WgJRQ3VitIsOw6Fzd3AAMAA2or9XFJVytIkChpE\nY3oer0B94kygg2Zl5Cfc1IpDn3yJ15ZMx6FPvuxRm0yU4kBRmPnc40hxFFYtmymW8Thw2GlLotaa\nwm3eAM7N71H5bDnZ8FefAZam4a8+g6xASXxQWmW8eGsb/vHCi7jvxguBuqNIBjtVsQNG51503y9Q\n/tCT2HnWdJyd6xdLc5maxAWsP9KEaTMmmYslUMLmlG9nc+LLXXvxhw+6e99Bl55bZzb7acuufRg1\nZaY68FKvbKeTLu5w2uDpxYiw0/j2cUqSpyfvmppRqriwHolsFRUVo/7YMfF3aQedmW46PQjbUwC8\nDiu+2t8CX5YNl10yV6YKKC9upIuf1gXwk8Pt+P2af+P9TeaymigAN44vRUWuC12hGCpyXbhpfA+B\n0ruwC6UkZVAmaQZebUMX2rr4L4S2rhhqG7S/PJXddzStji4QyBTt8MI+fBYoxoa8gZWyjjwjE7iy\nXCeYz7XiD/IGVqKsvAR3//BGvLHVYdqvpHrdJCpbVudmJIP64ZmZQOig1EoVFwh3LBxCVpb2a5NI\nxNHcRI6+ECDcAZOSkN1uN6KcRbO80Sfbi4b2oKaaQFEUKMZmqFbMnDwSm9rND5CeUV2C7amYZmCk\nEracbDnRyMkWyck5+V7c7g3gkDWF9Y44wlTPZ0XlbzIzkw49/qlrlr8HLsXi3et/giDFq025KQrX\nBwJglNtyHKwMjYVL3oWVoQ0VI63nHuZYvPPoYxi09s+oe+hnut2LJIgZUEvexf62DkyaPl62PBOT\nOAC0R2Jw2RjYXR5555zbuFtUIE1Udh+kOIAK8J2jia4OvP7L7/Wqg05rbp0ekqkUrNb0ez9Ngohl\nbgWI6eIUDYeDwR2Pr8vovE/jv4NTkjx1BaOaSpIWtIYIW2020Bb5Y1KFSak29WbOnddpFT0vXdEE\nCkvKsUpCjJQlFwdDa6pNJPj7lGDHZ/+SPSaEYSrN5G47g7JcF25+5EP43bwyVJ7rgtvOoLu5Hv6m\nzYbHU5bwSInkhf39mPrg+5j96DpMXvy+7v6U3XflBR7Z7zkeu24OVN7ASuQPqjQ0gWsRJWX8gZSU\ndVnzcThoMZzPZwZcMo5EVyusPnWHpBaMBkB3xZLY19SFS6vCxFRxAa2NdSgs1g7I7GhrxSfrPtJc\nTlEKYq3wcNjz+6Mz2IXVy2uIF6rSUdVoyBmqqyZwyThCh/bqqhVehw3dGXz2GZqCm6LRwqW0W/pz\nc3iCRFEY8tgvxRlubdt2Yshjv5SpUAxF4dpAAAt9fuy0JbHFnkCcEGdgtqNO8E+9ung6Nqz5J/4d\nbECtLYWbvQHMzCNnWsXb2tG6eRtWLZmO5vWbTMUSKJ87x3H4ezKM75/RH6lgp2H3IgmC52l+dQKT\nhw4h/L3Mm8QB4O+7j2DBnGkyL1w8GgNz1uWGwZjW3L64+dF/g7HQ6fIpjUONrShJkfPHVFCqUhl6\nnQCgobkNhblKX1kGA38J8+6i0SSevGuq4bFP47+PUzJ9i/8SN09ipDlQq5fXIJZKyW7WrrvxZtR1\n6V+kAN4A7nVYEYwmMlKjEkl+yGsiyaKhK4Yky4HpjmFrQxcmlwZQ6HWgMb2/Pj4HkikW4IDmUNwU\nebJnuZCIx8CxKVC0RSzlDMp3I5lkcaAtjOc3HBHN4rXN3Vhxz3noiiSxankNkkkWV1b1xfOxJA58\n9g9wuVWgLPxbgwJvFlcSpPvW7BQfl64j/Nx4eD+c4WOobRhmeP7K7rvahi7Z79LAyVcenIlst11F\nYrLddjTt34yax1/B60/ejlyfixhpcPmi15Drc6GlM4Q8P78Ol0ohmYoit6QMlF1dXrzpj+tRXuDR\nVc/MwBncAtdI4645AYmOJhyu34Dysy/QXe+373yOvFgQCZ33irP7OIrLB2ouj0WjcGaRVSEHY4HH\nYUdnRMOwT9Hw5eThJ7/9AO88dSPsrbWqVQry87D3UCfmLX6X94/YnCpzb7HXhb0v/BqlfQslF10K\ndJZbdhHu47ChPhJDX6c5FfDK6v7448YDuMzqJrb0542vRiLJonvXLviHD8WCZe9h1dIaZFeNQJKj\nsGDZe3ht8XRxNhwAuCkaNwYCaONSeL0ziFELL8UVP7odiYP1ePPia4mmchJCFIeHLl2IDrcNju4o\nrgn44dS7wKbPObtqJNq28WrzpM/eQyLJomPTFuz4/i1kJUoRZ/AFG8VI2gaflf+cG3UvauHAL+7H\nJ43tuO/CalPrayHFcuiIxpHn49VGavPfkHQHYDvrct33DACRbD1zz7mIR/ik+0RzHf61aQfmXTVf\nTuQtVhWxj+UPBePy8uNZBJKl8DpZTChP67fvwrhzZ8of5FgkYnGxJMdkOPA3GokjEU8iO/vbm815\nGr3DKak8SWEyX+6EhggDmY9jEZBkOYQTKdAUhXAiJRs+rPQ29fE6MH/RWn6CPU2h0OuAz8F/uRnl\n9AwaPR6elm8A8OrSwDw3vy+aUpnJhU673356ACwHXL30PXGd0jPPh6+JH1UgxBOQvE1CCU+6zm8u\nGSn+/LsbLkCyswn9880pNcruO+nvpGgDJVq7Yug7fBJ+fdfFeHzF/yHevAuFdD18qTrkllaIahLH\nAS3Nx7Hk8uH48VQPrhweh4+tY5HS+gAAIABJREFUB2WxAVY1OaAoiPP59NLPjcAlo0hFgrD6C0xv\n4237GkWjNFq2JchqP4DiIeoxL9L3TGvTcfTRUZ6ikQgcDjV5Em48ljy3ARRNk++cORZOC4UHFlZq\nlkfy/R7UH6jVDTkcd9YZ2NXULiNOggfKfeWPINwwXTR1FD5vNT8Y22GhUUoz2MPKb5BEL9CStQAF\nBEaNQLQ7hNcWT0ciyWL+0vdhZWi8tli71JdNWfCjAQNw+/33YOFdv8ebGz/F5oAF6x1xLLpsAe4c\nPBZf79uDme++gqG//Tn22VLYaE9gvSOO9Y44aq1J5CQp3Gh14JrsgD5xgty/lF01ArljR2Pe4rWg\nLZRxLEEaR9kkOjkW544rkT1u1L1Iwj8bWlBzEi7s6482YUKJIttJY5wPCULjAf3xS2JcQVt3BNm+\nnlIZMXJAR2HKxOsEALVHjqGivEz2WNLmhdVuSxvHTQz8Jfz9T8cVfDdxSpMnrVIcCUbGca0xLQLM\njmMhkSot79TAnCyZt6khGMWq5TVgOQ4syyEST2LGwDzMGpynmfMkXCDLRozGga940tMdS+JAS4jf\nF8upzOSCAhVUhGl2x5IoLqtAd3M92ETclLdJWOfrA62oLPbjjJKAuP7zv7ofo8Pb8cRVww1Jh7L7\nTvm7Xg6UgJv+uAE/erMR77QNwsonf4nNbz2FF379c1C2HsNl3sBK9C8vwZghxXh3jxeP/+pB9Bk3\nF5a8gaBo9ftIWVLsjecJABxtX8I78tyMtomHgrB7jA28Hc3H4c+XDz1VZoPNWXAtbHbtc8/iYvB4\n1KqbcOPx8G2TEI1ENFuufW4Hwge3a15obFYGyaNfE3N4BL9K+YXX4lB7t/g4neWBtbgCCRZw9BsA\n1xU8gfI7behKpjK6qFw0thSbUzGkFAbIlo1bsWppDawMzUeauF3YMHs+OjZtEctiqlKfAvHWNiT3\nHcRHqx/C1WdNxXU2B27yBXCF34tx2W64y4tx/aKXYOtXgMn9++Javx83+7Nxsz8b12QHMC3fS+6o\nIpTjlP6llo1bsXpZDdgUp+vlEtDNsfgsFcG148rVCw26F5VoiycQYVmMmqCXf2TcPQkAm+tacN7M\nyfwvEl+cbjCmEgK5ikewr74ZFX0kZn0tkmTUTaeRmk8C39Qp+TuaLdmlHzfjjTqN7w5OybIdYFyK\nE9aR2TROkMAfC0bBdMc0iZNeWY+0DcWx+ORwu8w0LpAhO0NjxsA8LFjMP7+bH/kQT99znrg8mmRl\nJb9PDrcjt6gUA90JTB0+AGW5LuxvCWHVljoEdbrwnt9whPc7xZK4/swSVOS58Wnu9fjtK39Fu3WK\nobepPZzA1/Wd4vy6l5fMwKtLZ2JnfRCVJTn4ZbQSlndeRsA1Hm3d8u0pCmIJTvozCWaiDYR1pONe\nXnlwJnI8Ttm23Y5ipLyl+NPDY3R9TML3oJHqZYTC/n5EnSNh9ZJTwkml0QGuMFryjedjkQiEVNF8\nbVmNqXTx7q4uFBQWqj4zAH/jEU1xYKPad85utwsHgiburHXGaqxeNhMcI71QcWAsNFIcx3/Ol85A\nJF3CK8ty4FA4ijKXuXZ2iqIw2eLEp6koplqzUPniH5A7djRaNm7Fp5NmYtjvHhYJSWjffuz4/i2y\nMp0RlOtTFIUsUMgKhjHc6cW/Xl6Clo1bsSMYMieXp8tzwjlKy3GyYwkES2uGHUWJ67Ich3eSIdxc\nXc5HHijWY7y+jMjT3xpacUvNWL0nAddlt8FZUoHIkVqE3lgBEPxhLaEosp38jEhuzCxYc/si0VLf\nQ5h0FCctvL91D2686bqeMp1OGc7etBuwWImlOWJJD1CV/5raOpDrl5AeilZ10ZFKdrwyZUciFhOJ\n1urlNeAStGE21Gn8b3HKKk9GpbhMVKlMoKc4ZVLWK/Y68N6qFzChr0tl8o0mWXRGk6IqFYol8fQ9\n56ExGMW4Ih8uGlqAqf2zVeGZY6dfhIKCQnGuXVmOS/ZVJZjIpT8LKpR0Ht6k0UNRNHA4AHnStXQ/\n0oynu/76FbYf68SrS2diR10nrnxhA+766w5sO9qB1x+5FKOmXIBI/QZ+W6FrjoZsZMlzGt11vYHR\nXDnAWMmSdgACwLRl5HXNnqezoEyViwVol0brtn2OolFnGe433HYcgXz5TLreRBUwDIN+fftqfmYo\nmgab0t6P1+NBV8h4zI+K7CnGajBsAvF0mCcb7kLk8D5wLIfVy2pkJvILz6nEl9KuO4oybLMfO7YY\nHVwKMZ9HHlvAsti28Aa5wqQzUFfjiZkbwJs+V12DN0XBVVGunRIuPRbHId7SqkmcpLEL/0iFMdXi\nhEv59zWYE0jC/u4ICuxWuO3aqozZrKe3dx/GwkvONzX8V5lQT/qZZTmE4wlY+58hK9PpluFIniYN\ntYpU/vtk43acNf17AOQKErGLTgBFg2KsvGLFWBFPpIhxBafx3cQpqzwB/B2xUnESPvdGqpQSLc3N\nSNBOWG365TstmC3rAT1E6197rBi+ZT0cxaOIFzipKuVzMIglWVw0tEBUFITZeMIFkqIoRJMsca6d\nYCKvyHOjtpkvjQg/P7/hiGoeXla/wagCsO1Aq6iICKTp3umDUdXPj21HO3Dfmp3gANz91x0q9URq\nKveOPAd+isKymUMwtiIXXZEEAm4bdtS2YGxFLkABVy3hlaKKQg/uuXg4xlbkYmNtC258dn3GquGN\nz65HttuOtu4Y/njTeIxNp3t//+n/pGca6itZ0nLdKw/OJB5fIFh651nY3y8SJOVrBsiT219dOlN8\nvVx5fWFTXGwo8H42aQn2rJEDwKb6q85NqWgaYfLUqfA57ZqfGYqidMtkHo8bQQPylO1xobUrhFyv\n4nlt/huSNieoeASDc/3Y09yJkYU8ueADFz3o6dziDeRudCEiJKanL/7eocMR3P016h7SDnxcOKY/\nXt58CGMJrfsZkaVMICU7WoqSoBC1tYvLI8Fuw2gFPUj9UZdXhTHAF8CYIWrSJu20W/XgdBw3UKA4\njsO/mtrxwBx9cm+c9UQhRNvAshz8LqeKSFMKxUmqSgEg/kxt/hu2HDiGMYMHgHF5cfOj/8Yz95wr\nU6DMIpZTAYai8MqSGT1qlYRQrV42U9zv/qP1uLisv6xUZ0ZBYix8ufjVpTNhs9LgQm1gSEO3T+M7\nh1NWeRIg/Y4U1Ca7xZKxQXx/7T407fvqhM7FbC6UQLTeefo2/GfDl7oXOKE8N2NgniptfN2hdmKc\nwdbGLtVcO6myNDDPjYHpn6VmctI8PCGkUSAAq687U+ZrEnxQ0nEiAqSPURQFf5YV4wbyhCTgtuHm\nRz4UR5Zs3McrRcFwHG/cPQXVA3Jw9dLe+4yEuXIDCntiD6orcvDirRNNVU3MmNSN/FDCqBo97xgp\n9qGqPAf9zpgs25dAfpWjeBirDTaC0RuAKgZDD0ZKLkVR4ngSEmw2OxIJ/ZDWgX1ysa9BI+MqfaE8\n+9zx2N4gJQoc2HBQJE7SEM1Chx31kVhGbfYeK4N+NIPXrv6BoZfp2wAxsFKiEFW9+py43Ol1Y8Ps\n+b0+R8Ef9YOzaTTu3I3zCMQJIHTaGYRl/qctiDOzPbCYaJrRznri/5bvuyuw8MFHeh7V8jgpVCmt\nn2Fz4uOd+zHtexcgEU/imXvPQyKezDjjSSRJi9aCsdA9HaQEj1QqxYKmKVAUhaTVjWSKNacgScp6\nHMchnkjysyNNzME7jf89TvivFI1Gceedd2L69Om44IILsG7dOuJ6X375JaqqqjBnzhzMnj0bV1xx\nxYkeWgapB8phYxBL6RvElag6Ywy2bNZuKTYLsynkx4JR1LaFwbh8CLZphyYapY0rvVIClGnjUmWp\nO8p/4Fctl6tTQglPWt4TICUAyRSr64PSgpQotHXF8NTd54ojS258dj0ue/xjeF02XLXkXTAWGi8v\n6b3PSFCF/nLXFHRHEli9vAY7altQVZZtmowZlfb0CJZ0xp9RLhapNKqElPyaHcUjmMan9HUilTRO\nnzdqqqA04kHEMoWL7OkSMOzsqdjfQJ71JiDgzkKXxvVGWQaae8FEfNramXGb/UXj+mNvKo6mlmbd\n9b4NkAIr5R10I9G27SuZ/+pE8MerrsX6exZhyh7tRgtA0mlnEJaZYFnsCoYw/ZxRJs+AnPVEZ7mB\nvCK89M5m9BtWJS/B6cQRCKqU1s+d7W1w2qyw2p2wpq8HVhuTebq4jCR1ysiXsvy37ZtajB46UFSd\nrl76HgB+0K8RhHVuf+wjvqnC5lOZxk+PZ9GGWf5x/PhxXH311aiursall14qW/bhhx9i7ty5mDVr\nFmbNmoUXX3zR1LFPuGz3wgsvwO124/3338fhw4exYMECfPDBB3A61XfDFRUV+Mtf/nKihyTiROMI\n7HY7opFIz/T4/wKSLIepM7+Hde/+Hd5plxLXMeNfURrHAX5Q7CaFIvX8hiMo8Nhx55QBuGrJWryy\ntAZ/2iYfxqos7z2/4QiqynOw7UCrjAA8/P4eInHSyoUScP+anSjJycLh1jDirRHRLJ7jseOei4eD\nYzmsWlaDDfuace+rWw2Jk5bRXKoKrV5eg85QDMPKcrDtYJtpMqZV2pMeUygPyjoFU3G0ffEmsifM\nFR+TljB7A2VZ1WgUj5R0zxhwHEMnnAO3Xz9IEdBuqkglU6AtEoKeNsRKyxRj/XZijo6AvIAPLUH9\nCwo3ZhZcO7vhmHMhom8+AwBizpOyDORm4wgn+fiPuod+ZlhukuL6seV4auN+zGPcavP0twyluVxJ\nqDI1q5PAcRz+nYrAR9GYNTzfzAZIdnaA8fl1S3h/a2jFBYXSkTPqHC4zYMNdePWZp/Da7+9Tl+g0\n8pyk5V0AxJ/f3rATl8+7Qm0Qz+jseOgZyaXv8U8378APbrlDVJJWLSMYxClC+U5iKn/m3vOIpnGH\n0waH45R213yrMMs/XC4X7rjjDoRCIfz+97+XLcvLy8Ozzz6LvLw8dHd3Y+7cuaisrMSYMWN0j33C\nytPatWtx5ZVXAgBKS0sxYsQIfPLJJ8R1v+28CqM7ZyNUjqpCy/6vT+gcMk0gD+TkIlfRZq6EctSG\nFEplSlCgvvr8QyQi8pBIDkBjVwy1zd14Zan6IkwBKPDYVQoH13kcZY4umUJCIgE0gMcuqdSceUcB\neGj2CDw7/wwsKAkj1vqVqBD9a8n5qB6Qg2uXv4cUy2kSJ6lBWznWRXoNbO2KYduhNlFx8mbZ8fWR\nDlSVZZvKa1IeR+uYwrHE15jjYD32AbwjpqpeeyEXKzvLKprHlYbxMeU5KNQwoCvLqtWE6AoBUtLd\ncLwZLp9x/o8eIpEwXFl8DpaspVpSemATMd3yiOFNSbo0897OJPZ2hEBneVSz7vgy0GJE/rESADAp\n14dPWzr4i38Gs9kcFhrnWJx4JxmSfy+ZSOs+YRDM5TJTeaZmdQVYjsPbyRCKaQazxvU33kBittdT\n8RoifNTD6ImCUZoynEWohXA8ifrPP0DxoU9lJTrlbDoVpKRK8XOKZdHY3oWiAl4BFRQiAOp8JwFG\nipRBuY9lWUSiMWRpBMwCis9LOg5FeCzhCKS77eJg4kH5jDvg9HgWA5jlH263G9XV1URRp7KyEnl5\neeJ65eXlqK+vV62nxAmTp/r6evTt21f8vU+fPmhoaCCue/jwYcydOxdXXHEF1qxZo7vfYDCIY8eO\nyf5p7Vf6nXwi/GzS1HPw6cfrer19sdeBQbluFGc4yHHS+TMxsqDHREvyqWj5orSUqf7DqhDZ/gFx\nG5K3SVCc7pwyAKFoQlQ4rqzqi4eunAzHvk/AJuKaygkF4LFLRmJUsU8zF0pa+rvq4ulw0ilkh7ei\nekCOWKpbuVi7VKckLtJIApLn6O6XNyORZDG8PAfJFIvhpX5TeU3S4zx/8/iMjulo/gzuIRPBeNQX\nYIEovX79eKz6/jg8MnsEAgo/1M+nDcLdUwdg8bSBqg+nUFYVkEzof7ELJd5gPGlIXOqOHRWfO/E1\n6W7hv3gI2TVCR5E90oaUTkceYPD5TJdm/vr72/DJP/8OgCN2azkvvFq8YE+cNBL7QxFwQMYdY2PG\nFmOoxYZ/p9IXYY2hwP8VKEzlvSVwcY7Dn5PdGGtxYOrYEuMNaBrFix6RvW6ksEyW4/B2QwtuuKAn\nmsBsNx0Jb359CHOH95cTIKmvKa8o48HA/95Ri3MqK1SPa4VgEkMzM8TGnXswbmR6e1Kuk+IxypUt\nEqZ5i9bCZmVw8yMfwmq3yT5LTDyoGs/S0NCguiYGzcSDfMdwMp9HJvzDDPbv348dO3Zg/Pjxhusa\nkqe5c+diwoQJsn/jx4/HhAkTwLLm2ymHDx+OdevW4c0338Tjjz+OFStW4IsvvtBcf+XKlTjvvPNk\n/xYsWAAAyMmREo2TF0lgtVoxYaJxezgJvU0gV0IZbmgGJGXKE8gBTdOIdKj9VMqLMCD31LgcVvz2\n4/3407Z6VOS5cc3S93HPnXfAtZ9MxgCeGA3v68OO2hasXl6Dbxq7VERL6f2xVExA0lOIu3/+IP54\n7xRs2Nus6zFSGrQ5Tj+DqSUYw6b9reA4YNP+VtGUbuSjkh6nemCu6WNmdW+HLbcIjoIy1T4pAKU5\nWajq58f8xXzye1U/PzhAfE3WbdyGr7Z+iXmL1sLrtCLfIALhw9XPay4T3kfjinymFN931ryl+1lK\nJBKwWa3aE+A5Fl53FoLd6pE4UnizHOgM6SdFOza+ic6tnxG7tUgX7JE+N3anqIxnswHA1LEl8FI0\nNqSiZDP3fxtmCZx0Jl8aXRyLPye7MYPJQvXYYlPHKv7Zw/AOGYavDrb3vG6EsMx/NLZiWn4ANskc\nUOnfJ1p30HTprjuWQHc8gaFnKUa6SHxNySSrH1lAwObaozjrAgXZ0grB7MXsOhI+2bQdE2tm87+Q\nPhuSx5IpViRMiTg/siWeSIolO+lnSYDHbYc1/XlcsGCB6pq4cuXKXp33/xKZPI+TxT/MoKmpCbfd\ndhuWLFkiKlF6MCymvvnmm7rLi4qKUF9fj0CA/6JpaGggsjaXqyfCv7i4GNOmTcOWLVswYcIE4n6v\nueYazJkzR/aYJf3BbW3tBqy0qaBMJUghgFJUjzsTAEzNupNCGVXQG0hLcKvS4YZaipNymfRnYdmE\nCy/Fur+8jKwpV6pa3JVQemoa06RA+pinZDCyOr5Gi3+4avuOcAI764MYUZ6DjnAcQwo9eGT2CFlb\nPqD2/jiLh2JHdyHOu+bHcI6agdYu7agIkkGb5DkSQFHA/au2it4lwask5D61h2LEmXWy4+zjyafR\nMXMCCURCFFzlZ6jPAz1RBcEI/x5JpljsqOtEezghviaRjX/Fn36zDKsnOhFPsAjFtcvP0a4OuDRK\nVNL30QsPnAOHTrI4wJceaIrS/SwlkkkwDP91wcSD4BK0KvQv4PWgrbMLAZ92onRFn1zsb2zFGQN0\nLu7xCPJcDjR1R5D/xgoxGBMgt79feG4VnqqncCltwctLZiC4e2dGYY8XjeuPP284iPVNDRhCiDD4\nb0JK4FYtmU72Pilm8nVs2oL3r7kBHybDuK26HFkmbyQZrw+eQYPFv3nwm13E1+1AKIIUx2HipBGq\nZaE3ngauuAPO4jJwl91OCMKkweQWINnSowb8ZedBfP/S6cRzEnxN3MhpmpEFJOypa8Kgojyiwqrl\nXcp0dp0SiXQThtXaQ7xInw0mHgSXtIC1eUXCZI11govTsHIsuJj6sySgubkLNE0hJ8eN1157DamU\n/DvB6/320si/aQnpfgdlCpfNgmGF3oyex8niH0ZobW3FD37wA9xwww2YMWOGqW1O2Ik2Y8YMvP76\n61i2bBkOHTqEnTt34je/+Y1qvebmZpHNdXR04LPPPsOdd96puV+v12v4xtAyiWsRJAdjgcPGIBpP\n9toXpQchgbzQbcegXHfGA4SFcS3CsN5xRT6iz4lkEAf4i+a4Ih8KvQ40dcWw7hDgzy/ErMI4zqoe\nJhrAtbjjCxuOIN9jF4kT0JNA3hVLos+IM7Fr7WuoKBmI2iBPcqTZTyP6erGnMYjBhV5ZblFHOCES\nJlKkgcUdgOfs+eBSCRT6HGg81PMFrjSEK4mLnqmblMEkzX1KJFNw2hm0dcUw9cH3IdzIaJEurWMK\nnXVWH/luRZnldONrm9ERSYivAwfAz7YhlV2IunAKFS7gYHuYSHaFrCfm4F4UVo0jHk9ayv1q1x4M\nHFZJXE9AV2cHfP6AbsMF30ghfUD9Ze/3edAe1Fcfhk6chI/XrpWTJ4JB+IIZZ+O9Dz7HJSPKVIpG\n6I0ViKSznwCAcXlQNWk0Zt36B7zz1I04vuJR3XMg4fIzy/DnDQfxx6uuxeT8vv8T4gQA8bZ2RILd\nWL28BpH2IOJt6s+/QLDmLebzgbZ2tmCLjcKdYypgyaDUKPc37cKx5feo1gknU3j/eBt+Pvdswh4o\nMLkFcBb3x7wl78kS4HnQ8N76S9hcLsRDIQSf/hnaIxEkWRYFfp2RLfGIyhxuhL9v3IW7fnyb9goS\nciSkhiMUhOXIll4RJwD4YtsuTKxSE0rlZ0NIEadiMXChIKzCh0uqNJEM5Qr06dNHd/mpgpP5PMzy\nDwEcx6mU+Pb2dvzgBz/AwoULcckll5g+9gl7nq677jp0dnZi+vTpuOWWW7B8+XJkpY2lTz75JF5/\n/XUAwPvvv4/vfe97mDNnDq666irMmTMH556b2awvEpSRBFqlB2WUgdF3jNGsOz2cSPnuy7pOcOlh\nvVIDuACSQdzB0GKZpo9PWGbH1P4BnF1zMbqa63Vb3IVoguvSnidpjpCyxDfk/MuRjEbE7ZTZT4ML\nvdhZHxRLcx3hhOZwYdk50BbQVt4rJpARkiFcIEBGqd5aGUzC4w/84TM47fz7IdtjR3mBR3bMfy0+\nHw8vGC0eU6vMJ4RgktLDBSjLlVLiJODwhg/Q/8xpRD+a+BqhJ+vJ1lmPghLCfLI0hFIuk9sXVeP0\n78TaW1uQk5uj23BhpgPVVzrUkDwV5eeivq1nqK/MICxBca4fDUFl6GbPnDSp74kNd2N61XCMK2wy\nFVWghcvPLIMNFN4+fhQcRcE1cAB5RS1P0kkwm9uyA3B63dh1sA3OgBdVrz4nL91JZvL93/1T8cRv\nf4uWXXtwXWVfnjiZSFqXQvA3kYgTx3FYdfQ4fnjBOEJHIm8W919zL+KRCDEIk8ktgM3l4ktVLheY\n3AK88dVBXHf5THMnZ5I4tXWHkWW3wW4zUXpTlutOABt27MKY8y7UX0nheUoyblUUwemZdr2HWf7B\nsiymTJmCO++8E3v37sXUqVPx1FN89thzzz2Hw4cP4/XXX8fs2bMxZ84cvPXWW4bHPmHlyel04okn\nniAuu+OOO8SfFyxYIHqWThaUSpJeGe9EowzMIpOkcSWaGupxbPsWNBbN0YwmUBrExxX50MfrQDLF\nYuGStVi5eIbYYTZiQC4sFgajzzoXNTUO7CO0uAsX5IF5bqRSrBhhoFXmoxkr/MXlqAJwuDEoKirC\nTLttR+UXroBGgrYRCvv7EciyylK+lYnhG2tbcP+qrWgJqomNVgaT8PivbjkbkRj/fmjriomlO2Wy\nuFZJUCR40E4Pl+K+NTtFhe5P150pW7fE0oKuvmWgGSvRjybAY2dQnp2FVDIJl9UCp9ViGLCqBYam\nkGQ5MDQFa6wLgQB/0df6XDAWi1im0ILP68GBLn3PE221g6PTXzuKuXZJhQLlsDKIJJJwWhnI5qQd\nO6RSO6JvPoOibXvwzw0fYZTfvHFZiQvHlWL95jp8Mvd83HXvPbCmKHxx1vmQypJaCeFas+hUkMyb\nUyLe2oa2bV9hWHUV5i1aKy/dSY6x9sWX8fb11+MCdwADB7jE/ZpNWhehMwz4742tmJjjQ8CpvlGR\nes9WL52BjpWPyEpzAJBsaUA8FOJLVaEQ9u7dA4/dioA7S/+cMsSb/9mB+VfNN7eyzoy7TJBKsWB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8S59QAQd+8STBp7J7Z+9Qaw/RMACH+uy3Lg7TMBxK414VMMisFBEIFMgYR1BQefn8Ghc4W4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\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Z2QpSsb4pk4GVMquEUJOadxiiuCHDSHUun0M2k1WdegVRRQRDPyNQPFQD8wiQgZa+sfCE\nkTzOOpKwgrjpZnh1rZGJD9knw7Ix46oIexQmfsmBaN2R96OP3G/aOk7luZH7zi3EPIh+iwFJntKM\nDk5U1Cp5iD306iwLzdkM6jkxI3R2YK2hcxgnkYEYNmbYfJIKdOdZmpK697RsRb7gVoiUlTIrcgOi\nfeYRahaiPUhSJFaFVOd6etTcdioigoLDhHeoqrzBKx+q5aK4QTKQFpMvym1nZzJw0y3y/mOSPO46\nYlpB7EwGhmFEI4cU4SPPs7LVJ674JSnJQl1X+bwWSFCdXlfHiWiYkQzHzeamHYQWBuQdyufd0IOJ\nB/pQqLWqNzn0TKByoKbtFPfA1NGjkkGFEKgQEl4pFh1Vb50xw+YT1zrEK5eT9L0na2AzAWXrqrfF\nhJqFrAQQDREJCltvGIk1DKCQLyAT4rarOixVrA6ymBbZQcux3AhB2popLhkQki/KbRcWdKyydiUk\nSQ7K8/d9X0kDS3Q9uVY1YF+kZ8XrUyWOTSWmLQ5oK2RkiHScnE5+e3bORW/zBasPIhIGJnnKOdoW\nCfoAkb1pJ+nKAkpWsk2d+UpsTJF9CQ9561cdL+yApEmbqlAjmXO9bccmG2H6TbUAL/staQFJAl4Q\nv8ziFEaoRdB1daquV0RiyXPluwW55YkhOtz0/qo2YqVtWRkQnfgb0paIPpIU/CoyIHH3hVoRIqy9\nLy0LVsqUFq6V7adKhqAo0J2rZyVDhJI1sWOxmIy8OC4zloS7dqNW1mBfq/YPIh4GJHkC9A5c9gAB\nwuuy8aBrsSCH2ZDGDAoFF91OdQaW7K1fdbywA1LHykFAx/MQs3+tysfUAllO9puO/lcchPVZLAZV\nzFlCHda3jqtTV4SUBv1cwS8iLSNPDNHhpvcH2hSkStsEVZ/pkJBy25Lgaa/oo9+9US4XwCBUDVp7\n7X1kWVDMMoxVu00S6M7VsxKMF6lkTRKElCodQ9yziWQVCiypKrFvgxgYMPzIynebD+vXd6JY9IGM\nuiWk3raQtlNwvSJcr1iJMaLLi7TUlR7qe66biraefFUQuOx7GVR0etg2uuNly3XPiAo1gWkCzdne\nftpzea3DOmulYKVMWCmzqu++QBSNI3bv6DWTfcoXXPT08VpYmCYqhI4F73kggp9R5i/bR9l3ZLyF\njz+O5jQwcZ/PhAxkVmKLSrEfHIuHShtBvwCC1zmd0gOHtC04LtK2Vfm7MibVb2xorL2voLLHWvdB\n9Xp6ncyaq9obJozGYZWfV79zPUhB5opcgmDfeH1F2V/tPVAYh+4TAPffYWO1uQ0wTQPDhjWqLSRB\nfOb0m/HhmngZpjS2GdmC1+78bmL9bW4MWMsTACCvFkRL3CRe0a9kfbXUZQKp82Fv6GFikDKoHP5s\nm0wqVTF7q1hIRG4X2srhuEWkNVNhc66HroK+m1QVsrIpOpY+mfuKECfakpKpsRVNpe+0yV8fu276\n/1HnLyNH0iy78nPl9bTBtm2FgTiFgwVv16puCtbNQa4DIHXZ0OrSdm4j/O6NgaDcxNPvVdYVlzhp\nWkVU9lh6r6KOUSG61S5MXpkSYl1yvSJcu7G6lqHAXUr3Fed+6rjMVMep9Ol0BqxNltM56J7bAjCw\nyVMZhiE+BGj3DQmcdL0iN41cVbkaiK/hE7qeCBmFogOyxy2Rva9dWyKOdQpuQLZf3ZIrKggrmxKl\nNiGJH9LN/EsCOqRP5GqtY9bNxm3lE5y/ajyU7wOu48KyNBXGqew24UEjOBTpf3PdPHZTIBOu6tCv\nimVpgVHfSrlL3drGINXAuhRFYVt5Lir3iu2XuZ6FVJ2daU8Cq0+7Zl5onBPpO6AYH9eFp2qxUh2H\n3ntBPF9/R/faj9C5ZmVif7rXfrS5l5QoBjx5qhxUgmKw5PO0nUK3U7KguF4xoDhNQ+VAihLgHUUz\nSZZRGFWDacmKdVKrhcrBn0S2Gu/gZj9TIQrkmlOunouUaQZiu5IiRzpK8Sz5kRESXnB3piy2ScdD\n0e16EqxTqBUPBR/ahYEjHGhVBzjv4DHMyqFMBCyrDiNBHFLaTuHt/2wsu+4U08n7Q9q4ccj/twAA\nHt9JREFU5l6GCXJW/Tus/4jij8rq7KpxTqK5MvF0uiV4lEmpQuwarz8iRzBobdpy0A9+K0SHYRiV\numWOW6wcOAQ8943v8xWnVSFzp8msX0loJpXWzC8SrNJfvuBiwrjhUkIWdvAnVb+Nd3Czn6kQBd8H\n8gUXs66dCtsypRlsUTLtdK1ImXLcmQohEQV3Txg3HMve31AO1q+u1ZekxUxVS8uIHIyrESTNORS5\nhV2Zg1J0INGuGDooeJdtW4Pp5JIDrd+UzmD3UgYJERJKAEjulZb4IxMgr6POTt+vMNcnb66l+5zX\ny5qLoPAtnZssIF5HALM/EPZBSDGg75Dv+xULjW2Z5ayaoCRBEu4b1pLFc6fxtJFkn6utL/h/npWt\nToOYhZERQkRE+yLKYIsKnnClLlHIWqkKYQnLYIty36NYkXTEPXnZcvmCi/E7DK1SQ1clcroIs2xm\nrRSMbBNcq167b62CvhyCQLLlxBpLYbob5GBtow7zIBETQjOzj/vvBKEa6yUrRyOTAOBaR8rXzLhq\nLgyrFPP252smc8mwKDYtcmkUyb3h9h3FdRdV4Vs0N6GlNCGr4SD6DQY0eQKow7bg4vbLjgzUvGPF\nEbnXhRxwrMiiyJ3GaiM1Z4PXsJ/rgj7IactXRtOSEpaxR4gI1+JFuUDbc+K903UpstY0HaJAk5se\n10V7Ll+RhGDnQVxqecWsQR2yxbMiRQWP5KoQuaTEP3maaDf95RWkbDvg5lGGij5QGayuUMEp/VwX\nHFeeBq6A3r4VM4gUrT1ago4yKK5DZe2iIG6Za4xrHSlfM3vm1Iq13kqZ1eKPIgsOqxSe0B5U+mb+\nH7n+XYIK37KAeBUXMb2P2q7yQfQZBjx5AsqHXPnAIcSCJlEiaw+bCs6C56IKU8uma53R1/A+114j\nVWSXzCGpAGjR4Uz+JrFhf75mciCDjb4e0D/ASXu6Fpyqa5BHbmhJAjZjjcQT6WSqxbEixQGvL9cr\n4u5rp8D1qn/5JiX+KdJEu/Crn4HveXqBxdKBJASIIkm0PlMimkmagbqh1h4NQUcZlPa0KrYnxH0X\nUgKGzgaT3Q/STrrnAguOjjp4Es9VZKHJpBW+2YB4jRJCAVX3gack9F+DLYI8EfBIlIq1R5T1JXJR\nyZ5nNsCXtCUSAYR8hBE3Hnjq1cTNk01bsRXAWZcdLwjaSplV2X9RMuSA4EFN14IT7WHYnhCw5Iv+\n/4Rxw5HXJJqytn31Yuj7JfKUMg24XjFUSiNqkgGvn5zrYeOajzC8wYqf1VQZiO/eCG1TRqJqzApr\nkBE9njWHpNyrjh9WhoYmFsruO9G66HgmooJtN4aSo7A951lwlNXBk3qumPVxoWOtSwokm09hXYPB\n5QMDWxR5IiC/8FWsPWFZX2EuKh54sTwi8hFFtZydf5jrTjUwmnbZqQZBR8mQo9ciqgVH7p+uxUdE\netlA9CRQ6/qILFSlNHjf66jV8/ppa2tDS3NTPKsPgzCNHm6wOI0E0r2VrB0haw5Yc5iUe924G34Z\nmhYusYhdtoQhLEr6Q4zCdxUoC07BccUlWiTK5LVUYI8knZHQmE62Vcmyph1cPojNgi2SPBGoWDDo\ng5VYJNiMOh1Fbrpf3hj0PJJwtYRZHKJqDqkGQYsy5FS1qUS14AgJ0iEmPNILlDS5ACQq9FmrGnlh\nUCGkLFTnKntWbNtGwXEAJPyGLgqoZXSaIlsiQoK8Va0dvDIxVeso/60T3yLrn16/at0/AIECyNJ1\nxdAfCq2HV7ZAkeSaqhgpztg1r+2mYOHT6UtnzFL2rCCmSqJrNhjz1H8xsMuzEISUaVHN2iLlTYj8\nweyZemVYwsCbh6isShJ980q8APx2DWkbhmHA9310FZwAwVNZP90u7prilMKhx857XuR+ZHOLu86k\n5QZUEDZXds8BBNrOueePOPKwQzByqxE1mR+vPEZNyoZEaBPpmhDRxUhzS1mAx1Q3EJQ+qSpDI4OG\nQCQhu9xyKhHWqFVOJaytZsmUqM9Z1PZLVqyjsj3blObT1e1ttvIsOx99CT5YvT6x/rYbPQzvPHpj\nYv1tbmwZlqe8/OAiGV0i8FxPMoHKqIiT9Relb9YqlEnxLQuyeBrV9felNU0G1pKVpKI4a5mJcu+S\ncvXp7mvYXNmEBPb+NTTUo6u7O9acZeAWBY5TxkLRqqQlpaDRbxISCAGdqmwrjPpWONlW8ThUv2nb\nUo+bUSAvqppQovnHGbtq/LDvNS2IWrFWEWQHSmTWq9a8EvTFznHQ+tQ/sWWQpxCEHVhRxBl1EPbs\n19IKkaPcaDJSkySJk8VH6c47ynzoMZNal4gU6ty7pFx9UQlY2FzZZAv6/jU2NKCzsyvahEUQub7K\n4pgkkDkSdNxngjge7oGdUMyX1tzMVDDrUCTkyM6tmMzvL94hr5M9Vovxed8TLSqVGDb631r3U6Kh\nJZt32k5Vk1lZ5mS532xdGk3NWfmcBrFZsMWTJ9UDK0mdHhqig46VAqgleOrdIkuVCDqB6Lxg7ygH\nvuo96Ks9jGvFYvuIgqRjrdjr6YzVgM5U81bo6GTiVmJAZEkgnyeReRVIxdedk0LqvrK1ijMGAHWr\nWtFDwSFacW41KdKZWxSICENf1WcLITiEYN919eSKFlU0/S+1PWPbC61iCmSWmzlJaallsxa+84tF\nSvMaRN9iiydPOode0hYg0UFHiES9bfWrbC0Rwgr48trTwd61DK4OK1WTZEZcElasuMWldZ5nei90\nRTXZfhsaG9HVlZDlSURMqM+FWVpUHyqopOIr1mJTzv7SEP6UjRE2JwI7txF+1wbY+aDAp8g6lghq\nSco41iPhd7LxqT21UiaU6xWyY+numaJgq+q+BYLEKfdoLufilu8fpje3QfQJtjjyxB4YJFBWVVU6\nyTnwDjpCJGZcVTLlnvOzBf0mW4vINLCfhRXwrdrzkMw91QB0lTayUjW1IG2JWSQjluwB1EhcQHuL\nQ5J09yaTyaAnl4ue9UZDYskgn/uuIzx0dIreKluwksj+ChtPw0XElW6wGoKfJamNpDB+UqSM7VtZ\noZ03PrOnVfUKFayAsSQByir4FaugQK8srA9aH4y+n7meAjrac9HnN4iaYYsiT7wDgyhX66hKJzUH\nQFz89a6rJwMAfnPx4YkHpkdB1kqhIW1XHbI84iMjQ6LvVA78KCrltOo2q3fFE/7sLyDzdtxiJEmG\nMBJMEyNRrJZIPoOHQqEAq15Np0YFImLClmepXpwJw7LhFf1SrTVZjJJmPIuopIkywtxLYbpV1Brl\n0g2ZSmZZTbSRakXKeOtgZBXild0J1ivUsgLGkMJI21a5rqoFN90S6WeErMF3nUrBa7KOAZgQ/1+B\nLYY8iQ4MWrm61s+galBx3vMCdaJUNZFqBTJvwzCqLEwi0cSwMjUiTSgRoqiUkyxB3/dhpcwqYUxZ\nrb7NiUwqBStlwvd92JapZRlTIZZVGl0CAsneJ2nfjoOGpsZkDtQwV0kYyUmZeOO99bBSJly7qdKn\nSuZSKGISkMB4QiuYXLeKxPDQBXhdu5HryuzTGKcYoBMA2DggWb09IcKsOwlaAaUo93P7ZUfCyedh\nZ9LRf0bK1k7Sz6BIZv/GFkOe/JwnPDCSzJyTzkHRPZV0Cn1ckPn4vl8la9CQtoXFlZOKH9NRKWdd\nizQRpdXgN5eIZRiq1OY5bl3RdYZRIg5esUQWZWuiiZGM6KpITBgG4HkOVm8oVFwLURHbVVI+rCaM\nGx4kIrIsqL4KbKbmGNkKxsTw0HXnZIKTSSPp8jcyJXNuvT1ObBOB6jMkqwUpzBiM8FIQkJSIS8gG\nRTIHDLYMkUwKRjYVOBBEisthq6bb6IoaRhGW7A+gf06JYOLd105ByjQSFQvlgRVxlAmKul4RrlcM\nWEx4ApBJCZAmDdFaw9aRL7gV16Su6KdIRJVVdRft1wtP/gtbjRqFPfeaAN91ImsvqYgrqqBUsiRd\nLVRIkaW4IpuRIVungtCjazcmLhjKG6cvSWWc+dPXAgbsTLoiOCkT6DQsuxxEHhxTNJdE9jhqADoz\n90GRzP6PLcbyBKCskC0PNlZxe4QF24ahPxEiHZB4Jtoy5vu+UkxMXITFRNGWEcMwAoHWomvzXu/n\nYfPWXZdO+zBNLTqRgLX8qNYYlEE1W1Jqocp3wHK6kOrZGP1gSbQuXhvfQkH6VC1RUgvI1qkQJA5U\nyxgkaQlKJFCaoNb7ylhiiFuMCE6K4uKEljqFTM+oz0tsbbIy2Hudydqx+htEbbDFkKdsXRotQ+oq\n6f88GQDTDHflqATb6iBKHbG4SILgkIO0q+CoxcTEABsbxhuHJXQFxwsEWotq+mVS4QRYd1067UVt\n2fmKXLmqNQZF0M2WFLqai0UY8PnZRBqIRAIEAoRSuYCGoZUsqFoWmhUhdqYeDzqp96rjiAQ3FaBa\nfDgWMWGIaO+/C2L9rvI1s2fyyauI2GoTe9XMzig/L+XxDcNAJmPpXz+ImmOLIE+GYSCbJRkPqUr8\nCy0DQLSH2KBiHmhfeZxsLZlApgopi0KC2HR9GVSUz5Msu8IDT6dJNA4hDt2OU7nPvLnoEGCddYUR\nD157nT3LuR7y5dqKMouQznOomy0pXItpwvM4sTxRoEFkuCn7illUWiVKqOsTAyeIWdZWK1ia05fS\nvaHGKTgujIah0e6lICOQOx82SFyTyPJiogBfulYZeRWJXALVFj8RVGPa4v68+L6PfD6aoO4gaost\ngjz5vo9czsXtlx1ZIUcFx8Ptlx1ZUXLOpi0sWbEOaTsl1Xxi67zRpVpUCYPK2z1dRyyT4gtN6lp5\nSHbZ6yvWhR7Wuv3XIsg9iiaU7wPFYngblWwznXXRliyV9lH0rQwDVXIL9DzZtqrQzZbkjZPJpFFw\n3b51hamm7NPgqTqrCmom6c7S7Vux5Anpx6trDfalYd2xCu3wuzZUSr5EzQyj97lKjDQkSFwbjMUz\ndn1BkcilCjh7zZWh0LS4tbkNVX8AIJ9z1OY1iD7FFkGeACDXU0Dbph50d+YDf+e6ChW9H5KhE6b5\nxHvTVyUbqocskSc47Zp5SkKTYdYp+pq9xg0HAC4pE/WvgqTqxBHE0YQKa6OababSV1U2oCdvTyvI\n0wKtKpY+HSKnG4fHjh9G5uhxrKYRyMGurStMIZ1clLJPgyYhNRHU1F1TSN9s0V1ZX6QfwzCCfenG\nkzESAVHuJV3+RkmDSlBDUGWvlQsTxySBSvvAqUnHlaGQZYAy8yREaRADB7F/Q8ycORNTp07FtGnT\nMGPGDCxdulTY9re//S2++MUvYtKkSbj11lvjDl0FkjjI/o28hx7X07IC0N/ruNlUD1kdoUkiG8Ae\nluRwqytLCdDWLNlcN4dUgmjPomhC8dqI1qnaX5jsAs/1xQN5BmgXcoajfi4Ct64ch1izyvRh5FqX\ncLHP8lYjhmHm756I5gpTgIjksLpJ0pR9GmVLhYi0xBXU1FqTzG0VUQXd9/2qvpTirOKWXOHpKwn2\nTiQFoKwoTl2jUpi4lpZDFqyYq+j+8lyEXl1JbDZvtwYsTIPQRy6Xw0UXXYRJkybh6KOPxqJFi6Tt\nC4UCjj76aHzlK18J9HHxxRfjuOOOw3HHHYfvfe976O7uDh07Nnk69NBD8cgjj+DBBx/E2WefjYsu\nuojb7qWXXsL8+fMxZ84cPPzww3jsscfw0ksvxR1eHfleC4SuKGUc/SbZoawiNJn3PGkW1pIV65Ap\nx8iQa8ibuWyu7NgqJUKiBoyHXSeSk+CB93lY8WVZH6pWtzBrE+mLPAO0CzlHSQyoxEmx8gH02nwf\nFZd0wfGkVlGaXOtaGtlnuXXYCNx06fHBAqeC2BttKLjiyN/CQGDuIuhDrVexWSqomRQxVHVb6aqg\nl/sRZj1qlnyJG39WmRdLZjgp+7yiz6LMN3ZNoYWJo1oOYwazkz6kRJR57ipixFlrUMcpJu644w40\nNjZi/vz5uO2223DFFVegp6dH2P6Xv/wl9t1338Bn9957L1zXxcMPP4yHH34YruvinnvuCR07dhj/\noYceWvn33nvvjTVr1nDbPfroo5g2bRrS6TQAYNq0aZg7dy7222+/uFNQR95Dpi6NbNZCLuci11NQ\nvjSX95D//+2dfUxT1xvHv22h5UVBahwUWNjilrjBEkY2N2Uy5aUtykvLMqkzMUMyXyZjMSYbMbgI\nugxYfjMKW7IYFpcfxMiWDW2wbobAzwmLsJH9wWQJZMEXis4oL25SoPT8/gCulL7d8tL2ds8nuWnv\nPc+55/me59xzz729PffRpMup8p3ZicViWCzWndV8qzVrVuLevYdWaSbRzEnMZAYzTXFp4+LHP0We\nPZbJlWsCP19nU0NWyCANlGBicgqP/h63aysSiRAUIrMpyxXW+bbyyhfkIEbzt892PPb84rMPALza\ngtW+TNZ209NjzJYXCJNpEqZ/Jjg/xmbSYYFNDF3ptlfnACCWiTBlYRCLRRBPMgSFBNjon5/XNG7b\nhlwxty0HioPw4ouJM20zlPPVPGWBedLi1rFkV7vpsX+myWCH9RMQMH1ym2IS/MPnit08hWCYIZNJ\nYWIRnJ/2ylsZFgyJROR237AYTbM+isbGZo4Lx5octen5zLbJ+dvCZUHYceTizBxhK3gdv9b5H7cn\nZ/nt+Tk3/3+Pqrh6AWBVRw41uqijNWtW4uHf4/zqe76/fOPEU6sz24CZCTu5Y1Go89r4CAaDAZWV\nlQCAuLg4JCQk4MqVK1CpVDa2v/zyC27cuIGCggL88ccf3HaRSASTyYTJyZk+e2wMUVFRLste0mee\n6urqsHnzZrtpRqMR0dHR3LpCocDg4KDDfY2OjuL27dtWy6y9WCxa0CKRiBEUFIDi/7QiKCgAEonY\nrfwiEb+y7dmFrpQhLDwIoStlTvPa0zcxPomHoyZMjE9abR83TXL/BhwfN1uVy9fXgAAJpIESFP+n\nFdJACQICJA41jY/bL8tVXczms0xZXOZzFKP520NCp6emCAoOtPGL7z74tAVnbSY4ZNqH4FDpjE0L\nZzOrc/bTUQydleOozgMkYhw88T9ulnF7NvO3T5icl8+nLc+2zbm+imf+6eruscS3jc+vH7FI5Nbx\nK5GIIZPZxm9+eRKJGBKJeMF9w0I0Oapnd9vh3IVrkyFSm/1bZu5Iu3P8unv8O/Jzbv4ps4Wrl7l1\n5EqjM58BuFXfC43TQmIy1/bQySsIkIjx98Nxt8oDgMHBQZtz4ujo8k3+GhMpR5xi9ZItMZHyJdfB\nd1wxNjaGTz75BGVlZTYDVp1Oh5CQECQnJ2PTpk0ICwvDtm3bXJbtcobxvLw8G2dmr2zb29u5q/+m\npibU1NSgvr4ecrncZj/79u2DVqvlRoQGgwF6vd7hs0/V1dWoqamx2paUlMTrdhpBEARB+BM7duxA\nV1eX1baioiK89957XvLIfUwmE1JSUjAyMmK13ZEOZ+OPtrY2vPTSS2hubkZERAQAoKysDHFxcXj7\n7bet8pSVleGFF15AXl4erl27hk8//RTffvstAKC1tRV6vR4VFRVgjOHQoUNISkpCQUGBczFsCfjx\nxx9ZRkYGMxqNDm3KysrYV199xa3X1tay8vJyh/YjIyPs1q1bVktnZyfT6XROyxEyRqORbdmyxS/1\n+bM2xkif0CF9wsWftTE2rU+n07HOzk6bc+LIyIi33XMLe+f1xejIyspi3d3d3PrevXvZpUuXbOyy\ns7NZamoqS01NZcnJySwhIYHl5ORweQwGA2fb1NTE9u7d67LsRT/z1NLSgoqKCpw5cwYKhcKhnVqt\nxscff4ydO3fCYrGgsbERH330kUP7sLAwhIXZPpjY1dWFKTcf+BYKU1NTGBgY8Et9/qwNIH1Ch/QJ\nF3/WBkzr6+rqQlRUFGJjY73tzqJwdF5fKCqVCufOnUN5eTn6+/vR3d2Nzz77zMbuwoUL3PeOjg5U\nVVVxd55iY2Nx9epVqNVqWCwW/PTTT3j22Wddlr3oZ54OHz4Ms9mM4uJiaDQaaLVa7pZcaWkpWlpa\nAADr169HRkYGtm3bhuzsbKhUKs8+LE4QBEEQhN9QWFiIkZERKJVK7N+/H8eOHUNISAgA4NSpUzh3\n7pzLfRw4cAAjIyPIyspCbm4uJicnsW/fPpf5Fn3n6eeff3aYdvz4cav1oqIiFBUVLbZIgiAIgiD+\n5QQHB+PkyZN204qLi+1uX79+PXfXCQAiIiJQXV3tdtl+M8M4QRAEQRCEJ5AcPXr0qLedcAeZTIZX\nXnkFMr7vIRIY/qzPn7UBpE/okD7h4s/aAP/XJ0RcTlVAEARBEARBPIZ+tiMIgiAIgnADGjwRBEEQ\nBEG4AQ2eCIIgCIIg3MDnB0/l5eXIzMyERqPBW2+9he7uboe2n3/+OTIyMqBUKh2+9sXXuHDhAnJy\nchAfH4/6+nqHdh0dHUhMTIRWq4VGo0F+fr4HvVwYfLUBQENDA5RKJZRKpc0UF76KyWTCwYMHoVQq\nsXXrVrS2ttq1E1Ls+vv7odPpoFarodPpcPPmTRsbi8WCsrIyZGRkQKVS4ZtvvvGCpwuDj76amhps\n3LgRWq0WWq0Wx44d84Kn7lNZWYm0tDSsW7cOfX19dm2EHDs++oQau+HhYezZsweZmZnIzc1FcXEx\nhoaGbOz49jmEB1jQnOgepLW1lZnNZsYYYy0tLSw9Pd2uXWdnJ8vJyWHj4+PMZDKx7Oxs1tnZ6UlX\nF0Rvby/r6+tjH374Iaurq3Nod+3aNfbGG2940LPFw1fbrVu3WEpKChsaGmKMMbZ7927W2NjoKTcX\nTE1NDSstLWWMMdbf38+Sk5PZo0ePbOyEFLtdu3YxvV7PGGPs/PnzbNeuXTY233//PSssLGSMMXb/\n/n2WkpLCBgYGPOrnQuGjr7q6mlVWVnratUXz66+/sjt37rDU1FTW29tr10bIseOjT6ixGx4eZh0d\nHdx6ZWUlO3z4sI0d3z6HWH58/s7T66+/DolEAgBITEzE3bt37dpdvHgRGo0GUqkUMpkMGo0GBoPB\nk64uiGeeeQZr167lXrDsDCawP0by1fbDDz8gIyMDq1atAgBs375dELEzGAzQ6XQAgLi4OCQkJODK\nlSt2bYUQuwcPHqCnp4d7o3hWVhauX79ucwVsMBiwfft2AIBcLkd6ejouXbrkcX/dha8+QBjxmk9S\nUhIiIyOd+i7U2AH89AHCjF14eDhefvllbj0xMdHmhbiAe30Osbz4/OBpLnV1ddi8ebPdNKPRiOjo\naG5doVDYbXxC5saNG8jLy0N+fj4aGxu97c6SMTg4KMjYudPmhBC7wcFBREZGcoNdsViMJ554Anfu\n3LGyE+qxxlcfMH2Sys3NRWFhIX777TdPu7psCDV27iD02DHGcPbsWaSlpdmk/RviJxQW/XqWxZKX\nl2cTfMYYRCIR2tvbuY6uqakJTU1NLp+d8TX46nNFfHw8WltbsWLFCty+fRsFBQWIjIzEhg0blsNt\nXiyVNl/Fmb62tjbe+/HF2BGO2bFjB/bv3w+JRIL29na8++67MBgMCA8P97ZrhAv8IXbl5eUIDQ3F\nzp07bdKE3qf6E14fPH333XcubS5fvoyTJ0/i66+/hlwut2sTHR0No9HIrQ8ODkKhUCyZnwuFjz4+\nhIaGct9jY2ORnp6Orq4ur56Al0qbQqHAwMAAty6U2MXExMBoNCIiIgLAtN+vvvqqjZ0vxs4eCoUC\nd+/e5QaIFosFf/31F6KioqzsZo+1hIQEANO6Y2JivOGyW/DVt3r1au77xo0bERUVhd7eXr94kblQ\nY8cXoceusrISN2/exJdffmk3fTZ+rvocYvnx+Z/tWlpaUFFRgdraWqcnVLVajcbGRkxMTMBkMqGx\nsRGZmZke9HR5uXfvHvd9eHgYV69exXPPPedFj5YOpVKJ5uZmDA0NwWKxoKGhAWq12ttuuUSlUnFv\n7e7v70d3dzc2bdpkYyeU2Mnlcqxbtw56vR4AoNfr8fzzz3Md9SxqtRoNDQ1gjOHBgwdobm6GUqn0\nhstuwVff3Ocqe3p6YDQa8fTTT3vU1+VCqLHji5Bjd+LECVy/fh1ffPEFAgLs39fg2+cQy4/Pv55l\nw4YNkEqlkMvl3BXjmTNnEB4ejtLSUqSlpWHLli0Apv+mev78eQCARqPBgQMHvOk6L5qamlBVVYXR\n0VFIpVIEBwejtrYWa9euxalTpxAZGYn8/HzU19fj7NmzCAwMhNlshlarxe7du73tvlP4agOmpyo4\nffo0RCIRXnvtNRw5csTnb1GPjY2hpKQEPT09kEgk+OCDD7i2KNTY/fnnnygpKcHo6CjCw8NRVVWF\nuLg47NmzB++//z7i4+NhsVhQXl6OtrY2iEQivPPOO3jzzTe97Tov+OgrKSnB77//DrFYDKlUiuLi\nYkGcoI4fP47Lly/j/v37WLVqFSIiIqDX6/0mdnz0CTV2fX19yM7OxlNPPcW9v+7JJ59EdXU1NBoN\nTp8+jTVr1jjtcwjP4vODJ4IgCIIgCF/C53+2IwiCIAiC8CVo8EQQBEEQBOEGNHgiCIIgCIJwAxo8\nEQRBEARBuAENngiCIAiCINyABk8EQRAEQRBuQIMngiAIgiAIN/g/hWLfhf9T7/kAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe2c8a4b650\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "zi = griddata(x, y, z, xi, yi, interp='linear')\n",
        "plot_contour(xi, yi, zi)\n",
        "plt.scatter(df.x, df.y, marker='.')\n",
        "plt.title('Contour on training data')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "hoANr0f2GFrM"
      },
      "outputs": [],
      "source": [
        "fc = [tf.feature_column.numeric_column('x'),\n",
        "      tf.feature_column.numeric_column('y')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "xVRWyoY3ayTK"
      },
      "outputs": [],
      "source": [
        "def predict(est):\n",
        "  \"\"\"Predictions from a given estimator.\"\"\"\n",
        "  predict_input_fn = lambda: tf.data.Dataset.from_tensors(dict(df_predict))\n",
        "  preds = np.array([p['predictions'][0] for p in est.predict(predict_input_fn)])\n",
        "  return preds.reshape(predict_shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uyPu5618GU7K"
      },
      "source": [
        "First let's try to fit a linear model to the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "zUIV2IVgGVSk"
      },
      "outputs": [],
      "source": [
        "train_input_fn = make_input_fn(df, df.z)\n",
        "est = tf.estimator.LinearRegressor(fc)\n",
        "est.train(train_input_fn, max_steps=500);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 502
        },
        "colab_type": "code",
        "id": "_u4WAcCqfbco",
        "outputId": "bd7ab880-73b6-4135-bfbb-73811747f175"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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SEhNxzY0zqD5cPeUGDEtPp/rA1rYbOWo0vnXtdVQfZs0powdPbG07R0ANoIjtGva2ncMP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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe36d4d0210\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_contour(xi, yi, predict(est))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XD_fMAUtSCSa"
      },
      "source": [
        "It's not a very good fit. Next let's try to fit a GBDT model to it and try to understand how the model fits the function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 522
        },
        "colab_type": "code",
        "id": "-dHlKFlFgHDQ",
        "outputId": "71847071-ae25-410a-af3f-b4ae877690b8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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D7nzwcUhvGSIKTuqE4KROTV5HrlCgQ6cMp/uKG8yIzchGrJPd1uv7bxWZ3h2R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lM2UBLmtpoyzcHVLqMEVxoUtzPJO21daA/KEEf4L5QBc5w2/MOaS3R32va392BaiROmwVUsvfrf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\n9GhzUwgfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fe36d8f5e50\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_trees = 22 #@param {type: \"slider\", min: 1, max: 80, step: 1}\n",
        "\n",
        "est = tf.estimator.BoostedTreesRegressor(fc, n_batches_per_layer=1, n_trees=n_trees)\n",
        "est.train(train_input_fn, max_steps=500)\n",
        "clear_output()\n",
        "plot_contour(xi, yi, predict(est))\n",
        "plt.text(-1.8, 2.1, '# trees: {}'.format(n_trees), color='w', backgroundcolor='black', size=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5WcZ9fubh1wT"
      },
      "source": [
        "As you increase the number of trees, the model's predictions better approximates the underlying function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cj8u3NCG-IKX"
      },
      "source": [
        "![](https://www.tensorflow.org/images/boosted_trees/boosted_trees_ntrees.gif)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SMKoEZnCdrsp"
      },
      "source": [
        "# Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZSZUSrjXdw9g"
      },
      "source": [
        "In this tutorial you learned how to interpret Boosted Trees models using directional feature contributions and feature importance techniques. These techniques provide insight into how the features impact a model's predictions. Finally, you also gained intution for how a Boosted Tree model fits a complex function by viewing the decision surface for several models."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "boosted_trees_model_understanding.ipynb",
      "provenance": [],
      "toc_visible": true,
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
